Search Results (14)

To address the challenge of low modulation recognition accuracy for Low Probability of Intercept (LPI) radar waveforms under low Signal-to-Noise Ratio (SNR) conditions—a critical limitation in current radar signal processing research—this study proposes a novel recognition framework anchored in an improved EfficientNet model. First, to generate time–frequency images, the radar signals are initially subjected to time–frequency analysis using the Choi–Williams Distribution (CWD). Second, the Mobile Inverted Bottle-neck Convolution (MBConv) structure incorporates the Simple Attention Module (SimAM) to improve the network’s capacity to extract features from time–frequency images. Specifically, the original serial mechanism within the MBConv structure is replaced with a parallel convolution and attention approach, further optimizing feature extraction efficiency. Third, the network’s loss function is upgraded to Focal Loss. This modification aims to mitigate the issue of low recognition rates for specific radar signal types during training: by dynamically adjusting the loss weights of hard-to-recognize samples, it effectively improves the classification accuracy of challenging categories. Simulation experiments were conducted on 13 distinct types of LPI radar signals. The results demonstrate that the improved model validates the effectiveness of the proposed approach for LPI waveform modulation recognition, achieving an overall recognition accuracy of 96.48% on the test set. Full article

This study explores biohydrogen production through dark fermentation, an alternative supporting sustainable hydrogen generation. Dark fermentation uses organic waste under anaerobic conditions to produce hydrogen in the absence of light. Key process parameters affecting hydrogen yield, including substrate type, microorganism selection, and fermentation conditions, were examined. Various substrates, such as organic wastes and carbohydrates, were tested, and the role of anaerobic and facultative anaerobic microorganisms in optimizing the process was analyzed. The research also focused on factors such as pH, temperature, and hydraulic retention time to enhance yields and scalability. Additionally, the study modelled the process using ASPEN Plus software 14. This simulation identifies the bottle necks of this process. Due to the lack of available data, modelling and simulation of the described processes in ASPEN Plus required certain approximations. The simulation provides insight into the key challenges that need to be addressed for hydrogen production. Future research should indeed explore current limitations, such as substrate efficiency, process scalability, and cost-effectiveness, as well as potential advancements like the genetic engineering of microbial strains and improved bioreactor designs. Full article

Despite rapid progress in UAV-based infrared vehicle detection, achieving reliable target recognition remains challenging due to dynamic viewpoint variations and platform instability. The inherent limitations of infrared imaging, particularly low contrast ratios and thermal crossover effects, significantly compromise detection accuracy. Moreover, the computational constraints of edge computing platforms pose a fundamental challenge in balancing real-time processing requirements with detection performance. Here, we present MonoSeg, a novel instance segmentation framework optimized for UAV perspective infrared vehicle detection. Our approach introduces three key innovations: (1) the Ghost Feature Bottle Cross module (GFBC), which enhances backbone feature extraction efficiency while significantly reducing computational over-head; (2) the Scale Feature Recombination module (SFR), which optimizes feature selection in the Neck stage through adaptive multi-scale fusion; and (3) Comprehensive Loss function that enforces precise instance boundary delineation. Extensive experimental evaluation on bench-mark datasets demonstrates that MonoSeg achieves state-of-the-art performance across standard metrics, including Box mAP and Mask mAP, while maintaining substantially lower computational requirements compared to existing methods. Full article

Insect pests have always been one of the main hazards affecting crop yield and quality in traditional agriculture. An accurate and timely pest detection algorithm is essential for effective pest control; however, the existing approach suffers from a sharp performance drop when it comes to the pest detection task due to the lack of learning samples and models for small pest detection. In this paper, we explore and study the improvement methods of convolutional neural network (CNN) models on the Teddy Cup pest dataset and further propose a lightweight and effective agricultural pest detection method for small target pests, named Yolo-Pest, for the pest detection task in agriculture. Specifically, we tackle the problem of feature extraction in small sample learning with the proposed CAC3 module, which is built in a stacking residual structure based on the standard BottleNeck module. By applying a ConvNext module based on the vision transformer (ViT), the proposed method achieves effective feature extraction while keeping a lightweight network. Comparative experiments prove the effectiveness of our approach. Our proposal achieves 91.9% mAP0.5 on the Teddy Cup pest dataset, which outperforms the Yolov5s model by nearly 8% in mAP0.5. It also achieves great performance on public datasets, such as IP102, with a great reduction in the number of parameters. Full article

The governance of rural living environments is one of the important tasks in the implementation of a rural revitalization strategy. At present, the illegal behaviors of random construction and random storage in public spaces have seriously affected the effectiveness of the governance of rural living environments. The current supervision on such problems mainly relies on manual inspection. Due to the large number and wide distribution of rural areas to be inspected, this method is limited by obvious disadvantages, such as low detection efficiency, long-time spending, and huge consumption of human resources, so it is difficult to meet the requirements of efficient and accurate inspection. In response to the difficulties encountered, a low-altitude remote sensing inspection method on rural living environments was proposed based on a modified YOLOv5s-ViT (YOLOv5s-Vision Transformer) in this paper. First, the BottleNeck structure was modified to enhance the multi-scale feature capture capability of the model. Then, the SimAM attention mechanism module was embedded to intensify the model’s attention to key features without increasing the number of parameters. Finally, the Vision Transformer component was incorporated to improve the model’s ability to perceive global features in the image. The testing results of the established model showed that, compared with the original YOLOv5 network, the Precision, Recall, and mAP of the modified YOLOv5s-ViT model improved by 2.2%, 11.5%, and 6.5%, respectively; the total number of parameters was reduced by 68.4%; and the computation volume was reduced by 83.3%. Relative to other mainstream detection models, YOLOv5s-ViT achieved a good balance between detection performance and model complexity. This study provides new ideas for improving the digital capability of the governance of rural living environments. Full article

With the development of science and technology, the traditional industrial structures are constantly being upgraded. As far as drones are concerned, an increasing number of researchers are using reinforcement learning or deep learning to make drones more intelligent. At present, there are many algorithms for object detection. Although many models have a high accuracy of detection, these models have many parameters and high complexity, making them unable to perform real-time detection. Therefore, it is particularly important to design a lightweight object detection algorithm that is able to meet the needs of real-time detection using UAVs. In response to the above problems, this paper establishes a dataset of six animals in grassland from different angles and during different time periods on the basis of the remote sensing images of drones. In addition, on the basis of the Yolov5s network model, a lightweight object detector is designed. First, Squeeze-and-Excitation Networks are introduced to improve the expressiveness of the network model. Secondly, the convolutional layer of branch 2 in the BottleNeckCSP structure is deleted, and 3/4 of its input channels are directly merged with the results of branch 1 processing, which reduces the number of model parameters. Next, in the SPP module of the network model, a 3 × 3 maximum pooling layer is added to improve the receptive field of the model. Finally, the trained model is applied to NVIDIA-TX2 processor for real-time object detection. After testing, the optimized YOLOv5 grassland animal detection model was able to effectively identify six different forms of grassland animal. Compared with the YOLOv3, EfficientDet-D0, YOLOv4 and YOLOv5s network models, the mAP_0.5 value was improved by 0.186, 0.03, 0.007 and 0.011, respectively, and the mAP_0.5:0.95 value was improved by 0.216, 0.066, 0.034 and 0.051, respectively, with an average detection speed of 26 fps. The experimental results show that the grassland animal detection model based on the YOLOv5 network has high detection accuracy, good robustness, and faster calculation speed in different time periods and at different viewing angles. Full article

The automatic opening of an ampoule bottle is key to making the operation of the sterility inspection automatic. During the automatic opening, the fracture characteristics on the neck of ampoule bottle need to be deeply understood to avoid the contamination of the samples by preventing the glass fragments from dropping into the ampoule bottle. This paper presents the calculation of fracture characteristic parameters, such as the coefficient K of the stress intensity factor (SIF) and the coefficient F of shape factor (shape coefficient), based on the finite element method (FEM) for a non-penetrating three-dimensional crack. Mechanical and mesh models were built for the special structure of an ampoule bottle with a V-shaped notch, and the influence of mesh size on the coefficient K was evaluated. The mathematical expressions of stress intensity factor and shape coefficient of three types of cracks were established. The results demonstrate that the crack ellipticity (a/c) and crack relative depth (a/t) have significant effects on the K_I, K_II, and K_III of the SIFs. The K_I plays a dominant role, which follows a symmetrical distribution at the symmetrical position on both sides of the deepest crack point, whereas the K_II and K_III can be negligible. The corrected shape coefficient F_I decreases with increasing ellipticity a/c and increases with increasing relative depth a/t under the combined tensile stress and bending loads. The comparison to the literature shows the calculation of the corrected shape coefficient has a high accuracy based on the FEM, which will be applicable and reliable for non-penetrating three-dimensional cracks. Full article

Hydrogen storage may be the bottle neck in hydrogen economy, where hydrogen spillover is in dispute as an effective mechanism. In this context, activated carbon (AC) was doped with nitrogen by using ammonia gas, and was further decorated with platinum, palladium, nickel, rhodium, iridium and ruthenium, via an ultrasound-assisted impregnation method, with average particle sizes of around 74, 60, 78, 61, 67 and 38 nm, respectively. The hydrogen storage was compared, before and after modification at both ambient and cryogenic temperatures, for exploring the spillover effect, induced by the decorating transition metals. Ammonia treatment improved hydrogen storage at both 298 K and 77 K, for the samples, where this enhancement was more remarkable at 298 K. Nevertheless, metal decoration reduced the hydrogen uptake of AC for all of the decorated samples other than palladium at cryogenic temperature, but improved it remarkably, especially for iridium and palladium, at room temperature. This observation suggested that metal decoration’s counter effect overcomes hydrogen spillover at cryogenic temperatures, while the opposite takes place at ambient temperature. Full article

Oxygen Reduction Reaction (ORR) is the bottle-neck strategic reaction ruling the fuel cell efficiency process. The slow kinetics of the reaction require highly effective electrocatalysts for proper boosting. In this field, composite catalysts formed by carbon nanotubes functionalized with palladium(II) complexes showed surprising catalytic activity comparable to those of a commercial Pt electrode, but the catalytic mechanisms of these materials still remain open to discussion. In this paper, we propose the combination of experimental and theoretical results to unfold the elementary reaction steps underlying the ORR catalysis. Full article

Increasing offshore wind farms are rapidly installed and planned. However, this will pose a bottle neck challenge for long-distance transmission as well as inherent variation of their generating power outputs to the existing AC grid. VSC-HVDC links could be an effective and flexible method for this issue. With the growing use of voltage source converter high-voltage direct current (VSC-HVDC) technology, the hybrid VSC-HVDC and AC system will be a next-generation transmission network. This paper analyzes the contribution of the multi VSC-HVDC system on the AC voltage stability of the hybrid system. A key contribution of this research is proposing a novel adaptive control approach of the VSC-HVDC as a so-called dynamic reactive power booster to enhance the voltage stability of the AC system. The core idea is that the novel control system is automatically providing a reactive current based on dynamic frequency of the AC system to maximal AC voltage support. Based on the analysis, an adaptive control method applied to the multi VSC-HVDC system is proposed to realize maximum capacity of VSC for reactive power according to the change of the system frequency during severe faults of the AC grid. A representative hybrid AC-DC network based on Germany is developed. Detailed modeling of the hybrid AC-DC network and its proposed control is derived in PSCAD software. PSCAD simulation results and analysis verify the effective performance of this novel adaptive control of VSC-HVDC for voltage support. Thanks to this control scheme, the hybrid AC-DC network can avoid circumstances that lead to voltage instability. Full article

Hereditary nonsyndromic sensorineural hearing loss is a disease in which hearing loss occurs due to damage to the organ of the inner ear, the auditory nerve, or the center in the brain that is responsible for the perception of sound, characterized by wide locus and allelic heterogeneity and different types of inheritance. Given the diversity of population of the Russian Federation, it seems necessary to study the ethnic characteristics of the molecular causes of the disease. The aim is to study the molecular and genetic causes of hereditary sensorineural hearing loss in Chuvash, the fifth largest ethnic group in Russia. DNA samples of 26 patients from 21 unrelated Chuvash families from the Republic of Chuvashia, in whom the diagnosis of hereditary sensorineural hearing loss had been established, were analyzed using a combination of targeted Sanger sequencing, multiplex ligase-dependent probe amplification, and whole exome sequencing. The homozygous variant NM_133261.3(GIPC3):c.245A>G (p.Asn82Ser) is the major molecular cause of hereditary sensorineural hearing loss in 23% of Chuvash patients (OMIM #601869). Its frequency was 25% in patients and 1.1% in healthy Chuvash population. Genotyping of the NM_133261.3(GIPC3):c.245A>G (p.Asn82Ser) variant in five neighboring populations from the Volga-Ural region (Russian, Udmurt, Mary, Tatar, Bushkir) found no evidence that this variant is common in those populations. Full article

Remarkable progress in radio frequency and micro-electro-mechanical systems integrated circuit design over the last two decades has enabled the use of wireless sensor networks with thousands of nodes. It is foreseen that the fifth generation of networks will provide significantly higher bandwidth and faster data rates with potential for interconnecting myriads of heterogeneous devices (sensors, agents, users, machines, and vehicles) into a single network (of nodes), under the notion of Internet of Things. The ability to accurately determine the physical location of each node (stationary or moving) will permit rapid development of new services and enhancement of the entire system. In outdoor environments, this could be achieved by employing global navigation satellite system (GNSS) which offers a worldwide service coverage with good accuracy. However, installing a GNSS receiver on each device in a network with thousands of nodes would be very expensive in addition to energy constraints. Besides, in indoor or obstructed environments (e.g., dense urban areas, forests, and canyons) the functionality of GNSS is limited to non-existing, and alternative methods have to be adopted. Many of the existing alternative solutions are centralized, meaning that there is a sink in the network that gathers all information and executes all required computations. This approach quickly becomes cumbersome as the number of nodes in the network grows, creating bottle-necks near the sink and high computational burden. Therefore, more effective approaches are needed. As such, this work presents a survey (from a signal processing perspective) of existing distributed solutions, amalgamating two radio measurements, received signal strength (RSS) and angle of arrival (AOA), which seem to have a promising partnership. The present article illustrates the theory and offers an overview of existing RSS-AOA distributed solutions, as well as their analysis from both localization accuracy and computational complexity points of view. Finally, the article identifies potential directions for future research. Full article

Monitoring process upsets and malfunctions as early as possible and then finding and removing the factors causing the respective events is of great importance for safe operation and improved productivity. Conventional process monitoring using principal component analysis (PCA) often supposes that process data follow a Gaussian distribution. However, this kind of constraint cannot be satisfied in practice because many industrial processes frequently span multiple operating states. To overcome this difficulty, PCA can be combined with nonparametric control charts for which there is no assumption need on the distribution. However, this approach still uses a constant confidence limit where a relatively high rate of false alarms are generated. Although nonlinear PCA (NLPCA) using autoassociative bottle-neck neural networks plays an important role in the monitoring of industrial processes, it is difficult to design correct monitoring statistics and confidence limits that check new performance. In this work, a new monitoring strategy using an enhanced bottleneck neural network (EBNN) with an adaptive confidence limit for non Gaussian data is proposed. The basic idea behind it is to extract internally homogeneous segments from the historical normal data sets by filling a Gaussian mixture model (GMM). Based on the assumption that process data follow a Gaussian distribution within an operating mode, a local confidence limit can be established. The EBNN is used to reconstruct input data and estimate probabilities of belonging to the various local operating regimes, as modelled by GMM. An abnormal event for an input measurement vector is detected if the squared prediction error (SPE) is too large, or above a certain threshold which is made adaptive. Moreover, the sensor validity index (SVI) is employed successfully to identify the detected faulty variable. The results demonstrate that, compared with NLPCA, the proposed approach can effectively reduce the number of false alarms, and is hence expected to better monitor many practical processes. Full article

The deformation of aeronautical monolithic components due to CNC machining is a bottle-neck issue in the aviation industry. The residual stress releases and redistributes in the process of material removal, and the distortion of the monolithic component is generated. The traditional one-side machining method will produce oversize deformation. Based on the three-stage CNC machining method, the quasi-symmetric machining method is developed in this study to reduce deformation by symmetry material removal using the M-symmetry distribution law of residual stress. The mechanism of milling deformation due to residual stress is investigated. A deformation experiment was conducted using traditional one-side machining method and quasi-symmetric machining method to compare with finite element method (FEM). The deformation parameters are validated by comparative results. Most of the errors are within 10%. The reason for these errors is determined to improve the reliability of the method. Moreover, the maximum deformation value of using quasi-symmetric machining method is within 20% of that of using the traditional one-side machining method. This result shows the quasi-symmetric machining method is effective in reducing deformation caused by residual stress. Thus, this research introduces an effective method for reducing the deformation of monolithic thin-walled components in the CNC milling process. Full article