Abstract:Stroke is a serious disease that causes high mortality and disability rates worldwide. Early and accurate imaging diagnosis is used clinically to achieve early prevention and timely treatment. However, traditional imaging diagnostic methods have a strong dependence on the knowledge and experience of doctors, which can easily miss unclear lesion features. To this end, a novel image segmentation model CS-SegNet has been proposed, aimed at automatically segmenting lesion areas in stroke CT images to assist in diagnosis. CS-SegNet is based on the UNet-Resnet50 architecture and introduces Channel and Spatial Attention (CASA) modules in the down sampling stage to enhance its ability to extract contextual information from key regions; in the up sampling stage, combined with the RDSConv module, residual learning and dense connections are used to optimize convolution operations, improve feature expression ability, and enhance segmentation accuracy in complex backgrounds; the skip connection part introduces a multi-scale channel attention (MSCA) module, which integrates low-level detail information and high-level semantic information to improve the accuracy and consistency of segmentation results. The experimental results showed that CS-SegNet achieved segmentation accuracy, average intersection to union ratio, and recall rate of 99.79%, 91.52%, and 93.83%, respectively, which improved the performance of UNet Resnet50 basic network by 0.14%, 5.11%, and 4.05%, and performed the best in multiple comparative experiments. Compared with existing mainstream models, this model has effectiveness, good segmentation accuracy, and learning ability in stroke lesion segmentation.

Abstract:Sugarcane is a significant and globally important economic crop, with a close correlation between plant height and yield. Traditional methods for determining sugarcane height characterization rely on labor-intensive and time-consuming manual detection. Therefore, this study collected image datasets of sugarcane crops in various scenarios and proposed lightweight enhanced PSPNet and YOLOv5s models for segmenting sugarcane bodies and detecting the tip positions. Additionally, an automated measurement robot was developed, deploying the improved models for real-time inference on images captured by a depth camera and implementing a gimbal automation system. Finally, sugarcane positions and depth information were utilized to calculate sugarcane height.The experimental results show that the average absolute error of the sugarcane height measurement method is less than 2.4 cm, the accuracy is higher than 97.61%, the success rate is higher than 93%, and the detection time is less than 13.2 s. In addition, the proposed sugarcane trunk segmentation and top detection model parameters are significantly reduced, with accuracies maintained at around 87% and 97%, respectively.

Abstract:The scarcity of abnormal samples of electronic connectors makes it difficult for supervised models to capture abnormal sample features, which largely limits the detection performance of supervised learning methods. Moreover, the existing unsupervised models have the problems of blurred reconstructed images and defects remaining, which seriously affect the detection accuracy. To this end, a one-step denoising simplex denoising diffusion probabilistic model electronic connector anomaly detection method that requires only anomaly-free sample training is proposed. Since Gaussian denoising diffusion probabilistic model has feature projection error in the image reconstruction task that leads to reconstruction position deviation, simplex noise is introduced to construct a simplex noise denoising diffusion probabilistic model, and the denoising paradigm is reformulated so that the inference time is reduced to 0.09 s. In addition, the research obtains an image preprocessing method that eliminates the interference of redundant features, so that the model learns the surface features of the electronic connector efficiently and improves the model learning efficiency. model learning efficiency. The experimental results show that the proposed method significantly outperforms the existing unsupervised models under the AUROC criterion, a standard evaluation metric for anomaly detection. The image-level detection accuracy reaches 99.71% and the pixel-level accuracy reaches 93.86%, demonstrating excellent anomaly detection performance.

Abstract:A high-precision measurement method based on machine vision was proposed to address the accuracy issues in offset detection, centroid positioning, and dimension measurement during chip placement. The method involves preprocessing the patch component images captured by a CMOS camera with grayscale conversion, hybrid filtering, and threshold segmentation. Sub-pixel edge detection was achieved using an improved Canny operator and Franklin moment algorithm. Depending on the offset angle of the component image, two edge segmentation strategies were employed to suit different measurement scenarios. The least squares method and RANSAC algorithm were then used to fit contour lines and obtain accurate contour line and intersection coordinates. Experimental results show that the method achieves an angle detection error of less than 0.05°, a centroid positioning error of less than 0.6 pixels, and a dimension measurement error within ±0.008 mm, with a relative error of less than ±0.1%. The processing time is approximately 21% shorter than that of the Canny-Zernike moment algorithm. The method offers high automation, fast measurement speed, and micron-level precision, making it suitable for real-time industrial chip placement detection.

Abstract:In order to solve the problems that the d-q axis current fluctuates greatly when the traditional sliding mode reach law control is applied to the vector control system of permanent magnet synchronous motor, a new sliding mode control strategy based on the reaching law is proposed. Firstly, a new sliding mode reaching law (NSMRL) algorithm is designed by combining the general power reaching law and the power function of speed error, and its stability is proved by Lyapunov function. Secondly, in order to further reduce the current harmonic proportion of permanent magnet synchronous motor and improve the current pulsation problem, this paper proposes to combine the extended Kalman filter (EKF) with a new reaching law to obtain the optimal current prediction in the sense of minimum variance. The simulation results show that compared with the traditional sliding mode control reaching law, the controller using NSMRL can reduce overshoot by 21.73% during startup and speed drop by 31 r/min after sudden loading. After the introduction of EKF, the d-q axis current fluctuation is reduced by 92% compared with NSMRL.

Abstract:To address the challenges associated with long cycle times, low efficiency, high manufacturing cost, and significant energy consumption in the optimization design of transformer, a multi-strategy improved particle swarm optimization algorithm has been utilized. This algorithm is used to optimize the parameters of amorphous alloy dry-type transformer (designated as AMDT) in combination with an optimization system developed on the Visual Basic 6.0 software experimental platform. During the particle initialization stage, the Logistic-Tent chaotic map is applied to improve the initial diversity of the particles. Additionally, the dynamic learning factor and the nonlinear dynamic inertia weight coefficient are developed to improve the local optimization accuracy and enhance its global optimization ability. The optimization of the SCLBH19.400/10 amorphous dry-type serve as a case study, the particle swarm optimization, quantum particle swarm optimization, adaptive particle swarm optimization, chaotic particle swarm optimization, and multi-strategy improved particle swarm optimization algorithm are used to optimize the parameters. The experimental results show that compared with the traditional artificial design scheme, the traditional particle swarm optimization algorithm, and the other three improved particle swarm optimization algorithm optimization schemes, the multi-strategy improved particle swarm optimization algorithm significantly and improve computational efficiency. It achieves a reduction in total loss associated with amorphous dry-type transformer by 15.41% and decreases the main material cost by 14.81%. These results substantiate the effectiveness and superiority of the multi-strategy improved particle swarm optimization algorithm.

Abstract:In weakly supervised object localization tasks, using hard fusion to combine deep and shallow features can cause the network to overly focus on discriminative regions or mistakenly identify the background as the object. To address this issue, this paper proposes a weakly supervised object localization method based on soft fusion of deep and shallow features and positive-negative sample contrast. First, the proposed soft fusion strategy for shallow and deep features generates foreground prediction maps from both shallow and deep features by designing a foreground generator. Then, a reverse supervision operation is applied to guide the network in gradually learning multi-level fine-grained features, achieving mutual optimization between shallow and deep features. Second, based on the concept of contrastive learning, a positive and negative sample contrastive loss function is proposed. By constructing positive and negative samples, the network is guided to focus more on the foreground regions during training while suppressing background noise interference. The effectiveness of the proposed method is validated on the CUB-200-2011 and ILSVRC-2012 datasets, achieving localization accuracies of 95.77% and 72.90%, respectively. The experimental results demonstrate the effectiveness and applicability of the proposed method in weakly supervised object localization tasks.

Abstract:To address the issues in the dung beetle optimization algorithm, such as falling into local optima and insufficient global search capability, which lead to suboptimal performance in 3D UAV path planning, a multi-strategy improved dung beetle optimization algorithm was designed. A 3D spatial model was constructed, and a comprehensive evaluation function was developed by considering factors such as path length, threat, altitude, and smoothness. First, a hybrid chaotic sequence was employed to enhance the initial population diversity. Then, during the dung beetle rolling stage, a “differential mutation” operator was introduced to improve the algorithm′s local search ability. This was combined with an improved sine algorithm to update individuals via a probability switching mechanism, further enhancing the global search capability. Finally, an improved spiral search strategy was incorporated during the breeding stage to strengthen the algorithm′s ability to escape local optima. Through optimization of six benchmark functions and analysis of particle motion trajectories in the search space, the results demonstrated that the improved algorithm performed better in terms of convergence speed, accuracy, and robustness. When applied to 3D UAV path planning, the optimal, average, and worst values of path length improved by 0.41%, 5.67%, and 18.03%, respectively, further validating the effectiveness of the improvement strategies and the superiority of this algorithm in practical engineering applications.

Abstract:In the study of wind turbine fault early warning based on deep learning, aiming at the prediction accuracy of the model and the accuracy of fault early warning, a combined model early warning method combining CNN, BiLSTM and attention mechanism Attention is proposed. Firstly, aiming at the problem of low quality of SCADA raw data, the parameter-optimized DBSCAN algorithm is combined with the control principle of wind turbine to complete data cleaning, and the GRA analysis method is used to screen the original features to reduce the redundancy between features. Aiming at the problem of model prediction accuracy, in order to improve the feature extraction ability of BiLSTM network and the focusing ability of key features, CNN and attention mechanism are introduced respectively to build a combined network model. Finally, the exponential weighting method is used to smooth the power residual, so as to determine the early warning threshold and realize the fault early warning of wind turbines. The effectiveness of the method is verified by the SCADA data of a wind farm. The experimental results show that compared with the BiLSTM model, the error indexes RMSE and MAE of the proposed model are reduced by 29.8 % and 30.7 % respectively, and the fitting degree R2 is increased by 4.8 %. The warning time is 2~6 hours earlier than the SCADA alarm log.

Abstract:In order to solve the challenges faced by target detection algorithms of UAVs in transmission line insulator inspection, such as high model complexity, insufficient accuracy in detecting defects of small targets, and easy feature loss during up and down sampling, this paper proposes a lightweight improved RT-DETR insulator defect detection algorithm based on lightweight improvement (SDH-DETR). Firstly, RT-DETR is used as the baseline algorithm to reduce the optimisation difficulty and improve the robustness; secondly, lightweight StarNet is used as the backbone network to improve the feature extraction capability while significantly reducing the model complexity; next, the DySample dynamic upsampling module is introduced to efficiently reduce the detail loss and image distortion by the adaptive upsampling method based on the sampling points. Finally, the Harr wavelet transform downsampling module (HWD) is used to achieve efficient fusion of low-frequency and high-frequency information, suppressing complex background interference and enhancing the detection of small targets. The validation experiments on the complex background dataset show that the average accuracy of SDH-DETR reaches 98.5%, which is 0.9% higher than the baseline algorithm, the number of parameters and computation are reduced by 43% and 46.1%, respectively, and the detection speed reaches 78.6 fps. This indicates that the algorithm achieves a lightweight design while ensuring high accuracy, and meets the practical demands for efficiency and performance in transmission line inspection.

Abstract:Existing fault diagnosis methods predominantly adopt a “single signalsingle model” dedicated architecture, requiring independent diagnostic models for different sensing signals. Such approaches face practical limitations including limited model generalization capability and insufficient adaptability across signal types. To address these issues, this paper proposes an intelligent diagnostic method based on a unified deep network model applicable to both vibration and acoustic signals. First, the method utilizes an improved gold rush optimizer algorithm and envelope entropy fitness function to optimize variational mode decomposition (VMD), enabling adaptive determination of the intrinsic mode function (IMF) decomposition number k and penalty factor α. Subsequently, the average kurtosis criterion is employed to screen VMD-decomposed IMF components, followed by secondary denoising and reconstruction using improved wavelet threshold denoising to enhance fault features in acoustic-vibration signals. Then, building upon the Transformer architecture, a deep residual shrinkage network is introduced to construct local feature extraction layers, thereby improving the model′s capability in local feature extraction. Concurrently, a multi-scale linear attention mechanism is designed to replace the multi-head self-attention in Transformer, reducing computational complexity while strengthening the model′s ability to capture long-range dependencies. Finally, experimental validation on a self-constructed rolling bearing acoustic-vibration dataset demonstrates the superiority of the proposed method, achieving 90% diagnostic accuracy for acoustic signals and 99.77% for vibration signals, outperforming comparative models including ResNet18, DRSN, ViT, MCSwin_T and WDCNN.

Abstract:To address the difficulty of analyzing qualitatively and measuring quantitatively the correlation of underwater acoustic array element domain data before and after angular domain separation through experiments, a simulation method is proposed to perform correlation simulation analysis on the input and separated array element domain data in the azimuth and distance directions. Some typical seafloor echoes are used as inputs to the linear array from different directions as the simulation model. The correlation index between the input and the separated data is simulated. By pre-emphasizing on the ends, the simulation results show that the data of the typical seafloor echo input has better correlation with the data of the angular bandpass filter output in the azimuth directions. The results show that the correlation coefficient of reef bottom echoes has increased from 0.988 1 to 0.999 8, the correlation coefficient of sand and mud bottom echoes has increased from 0.934 2 to 0.996 7, and the correlation coefficient of mud bottom echoes has increased from 0.838 8 to 0.958 1.

Abstract:This paper presents a template matching-based blind recognition method for MQAM modulation. The method begins with estimating the carrier frequency, bandwidth, and code rate, followed by completing preprocess. Subsequently, it defines the constellation distribution entropy to describe the convergence degree of the constellation diagram distribution and achieves optimal sequence acquisition and residual carrier compensation through optimizing constellation distribution entropy. This process leads to the restoration of the optimal constellation diagram distribution. Finally, it calculates the matching degree between the optimal constellation diagram distribution and pre-set templates for different modulation types in order to accomplish modulation style recognition, including 16QAM, 32QAM, 64QAM, 128QAM, and 256QAM. This method is insensitive to initial parameter selection and leverages essential differences in constellation diagram distributions for robust recognition. Simulation results indicate that at a signal-to-noise ratio (SNR) of 15 dB, the recognition rate surpass 93%. Additionally, the proposed method exhibits significantly improved noise resistance compared to existing algorithms-thus validating its effectiveness.

Abstract:In wireless sensor network (WSN), in order to improve the energy efficiency of sensor nodes (SNs) and extend the service life of WSN, UAV is usually used. UAV as a data collector. However, due to the characteristics of high energy consumption in traditional UAV flight trajectory setting, in addition, some nodes have poor communication channels in geographical characteristics. To solve the above problems, a WSN energy-saving data acquisition method based on IRS assisted UAV trajectory optimization is proposed. The method uses IRS to enhance the UAV′s data acquisition capability through the signal sent by the reflection sensor. The energy efficiency of WSN nodes is improved by optimizing UAV trajectory. Specifically, the IRS-assisted UAV air-ground channel model is first constructed, and then the WSN energy consumption objective function is constructed for UAV flight trajectory and wake-up mechanism constraints. By using block coordinate descent technology, the objective function is transformed into an optimization problem for UAV flight trajectory and SNs wake-up scheduling strategy. Through simulation, compared with the three typical UAV trajectory optimization methods, the energy consumption generated by this scheme is reduced by 91.0%, 61.5% and 41.6%, respectively, which proves that the WSN energy efficiency is significantly improved by this method.

Abstract:In order to achieve stable control of the swing mirror system over a wide temperature range, a design and optimization method based on capacitive angular displacement transducer is proposed. By analyzing the transfer function of the swing mirror system and the temperature characteristics of the capacitive angular displacement transducer, high-frequency composite ceramic materials are used as the sensor medium, and temperature drift suppression measures are taken through symmetrical differential measurement circuits and automatic gain control circuits to reduce the impact of temperature changes on the sensor output signal. Build an experimental platform to test the output voltage and deflection angle of the sensor within the temperature range of -40 ℃ to+60 ℃. The results show that the improved capacitive angular displacement sensor has a maximum temperature drift of 0.87 V, a maximum angle drift of 0.63′, and a temperature drift of 11.67 ppm/℃ in a wide temperature range, which is significantly better than the performance of traditional capacitive sensors. The temperature drift suppression measures adopted can effectively reduce the impact of temperature changes on the control accuracy of the swing mirror, and are suitable for high-precision swing mirror systems in a wide temperature range.

Abstract:In the foggy scene, the captured image is blurred, the detail information is missing, and the target and the background are difficult to distinguish. Aiming at the problems of missed detection, false detection and slow recognition speed of existing deep learning target detection algorithms, a real-time object detection algorithm based on YOLOv7 in foggy weather is proposed. Taking YOLOv7 as the baseline, a cyclic defogging double sub-network including AOD defogging sub-network and foggy image generation sub-network is designed at the front end. The lightweight AOD defogging sub-network takes up little computing resources, effectively overcomes the negative impact of foggy days on the image, and enhances the feature extraction ability of the model. The fog image generation sub-network improves the dehazing performance of the AOD sub-network in the model training stage, and does not participate in the calculation during the test, which significantly reduces the inference time. The improved image reconstruction loss function introduces blurred image information, and the overall network unified training effectively combines defogging and detection tasks. The CityScapes data set is integrated into two foggy image data sets with different fog concentrations. The experimental results on the two data sets show that the average accuracy of the method is 65.2 % and 64.2 %, and the detection speed FPS is 42.4. The model accuracy is the best among all the comparison methods and can achieve real-time detection. Finally, the trained models are verified on the RTTS dataset, and the generalization ability of the designed model is better than other methods.

Abstract:Aiming at the issues of low statistical accuracy and performance degradation of existing coal mine underground drill pipe counting methods in low-light environments, this paper proposes a low-light image drill pipe counting method for coal mine underground based on improved YOLOv8. This method calculates the number of drill pipes by detecting the center point coordinates of the two prediction boxes of the drill chuck and the holder, drawing the spacing curve, and counting the peaks. Firstly, the SCI module is used for pre-processing low-light images to address issues such as uneven illumination and low contrast, ensuring that the subsequent model can extract more effective feature information. Secondly, the EMA attention mechanism is integrated into the C2f module in the backbone network to retain information from each channel and establish long- and short-term context dependencies, enhancing the focus on targets in low-light and complex backgrounds. Additionally, the BiFPN structure is used as the feature fusion method in the neck network to reduce feature information loss and enhance the network′s feature fusion capability, improving the detection accuracy of the model in low-light scenarios. Finally, the Inner-CIoU loss function is designed, based on auxiliary bounding box regression of different sizes, to enhance the model′s adaptability to low-light and noise. Experimental results show that the improved YOLOv8-GC algorithm achieves a 5.7% increase in mAP@0.5, with a detection speed of 151 fps; the drill pipe counting accuracy in low-light environments reaches 97.2%, fully demonstrating the effectiveness and application potential of the proposed improved algorithm.

Abstract:Multimodal medical image fusion is a computer-aided diagnostic technique designed to integrate effective feature information from different modalities, serving clinical diagnosis and treatment. To address the deficiencies in edge feature preservation and saliency energy perception in existing multimodal medical image fusion methods, this paper proposes a medical image fusion algorithm based on hybrid multi-scale edge preservation and deep image prior-guided illumination saliency decision. First, the truncated Huber filter (THF) is utilized to decompose the source images into a saliency energy layer and a coarse-scale detail layer. Multi-level decomposition latent low-rank representation (MDLatLRR) is then applied to smooth the saliency energy layer and extract fine-scale detail layers. Second, for the base layer, a fusion rule based on illumination map decision guided by deep image prior is used to enhance the visual perception of the fused image. For complex scale edge detail layers, high-frequency nuclear energy mapping is employed to calculate correction weights for fusing the detail layers. Finally, the fusion result is obtained by linearly reconstructing the components. Experiments demonstrate that the proposed method outperforms other state-of-the-art methods in terms of subjective visual quality. Moreover, it achieves average improvements of 6.42%, 16.33%, and 12.58% in the objective metrics QW, QP, and QAB/F, respectively.

Abstract:The intelligent harvesting of green citrus relies on fast and accurate detection technology. To address the issues of insufficient detection accuracy and missed detection caused by the diverse sizes of green citrus, complex orchard environments, and high similarity between fruits and backgrounds, this study proposes a lightweight and high-precision green citrus detection model (RT-GCTR). The model employs a large receptive field wavelet convolution module (WCLRF_Block) to enhance multi-scale target feature extraction. It integrates a multi-scale multi-head self-attention mechanism (MSMHSA) to construct a multi-scale fusion module (MSMH-AIFI) for adaptive feature aggregation. Additionally, it introduces SPDConv and CSP-OmniKernel modules to design the SCOK-CCFF feature pyramid, improving small target detection accuracy. Experimental results show that RT-GCTR achieves AP50 scores of 92.0% and 92.2% on training dataset 1 and test dataset 2, respectively, outperforming other advanced models. Compared to RT-DETR-r18, it reduces parameters and computations by 26.7% and 25.4%, respectively, and achieves a detection speed of 10.3 fps on the NVIDIA Jetson Orin NX. This study improves accuracy and real-time performance while reducing complexity, making it suitable for edge device applications.

Abstract:Traditional contact strain gauges present limitations in applications such as aerospace structure testing, including susceptibility to introducing interference and lacking full-field measurement capability. To overcome these challenges, this paper studies and implements a non-contact, full-field strain measurement method based on digital speckle image technology. A stereo vision system was used to collect images of an equal-strength beam specimen with a random speckle pattern prepared on its surface, under loads ranging from -20 N to +20 N. By employing a digital image correlation algorithm to calculate the displacement field of the specimen surface, the average axial strain in a specified area was subsequently obtained. The digital speckle image measurement results were systematically compared and analyzed with the measurement results from a resistance strain gauge system (full-bridge configuration) attached to the same area of the same specimen, as well as theoretical calculated values. Results show that within the range of ±176 με, the average measurement error of the digital speckle image measurement system was reduced by 5.3% compared to the strain gauge measurement system. Furthermore, through linear fitting analysis of the measurement data, the digital speckle image measurement system exhibited certain advantages in terms of sensitivity and zero stability; its strain-vs-load curve was closer to an ideal linear relationship and showed no zero offset.