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Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To tackle the challenges of path planning for unmanned aerial vehicle (UAV) in complex environments, a global–local balanced whale optimization algorithm (GLBWOA) has been developed. Initially, to prevent the population from prematurely converging, a bubble net attack enhancement strategy is incorporated, and mutation operations are introduced at different stages of the algorithm to mitigate early convergence. Additionally, a failure parameter test mutation mechanism is integrated, along with a predefined termination rule to avoid excessive computation. The algorithm's convergence is accelerated through mutation operations, further optimizing performance. Moreover, a random gradient-assisted optimization approach is applied, where the negative gradient direction is identified during each iteration, and an appropriate step size is selected to enhance the algorithm's exploration capability toward finding the optimal solution. The performance of GLBWOA is benchmarked against several other algorithms, including SCA, BWO, BOA, and WOA, using the IEEE CEC2017 test functions. The results indicate that the GLBWOA outperforms other algorithms. Path-planning simulations are also conducted across four benchmark scenarios of varying complexity, revealing that the proposed algorithm achieves the lowest average total cost for flight path planning and exhibits high convergence accuracy, thus validating its reliability and superiority. [ABSTRACT FROM AUTHOR]

Overhead transmission lines are widely deployed across both mountainous and plain areas and serve as a critical infrastructure for China's electric power industry. The rapid advancement of three-dimensional (3D) laser scanning technology, with airborne LiDAR at its core, enables high-precision and rapid scanning of the detection area, offering significant value in identifying safety hazards along transmission lines in complex environments. In this paper, five transmission lines, spanning a total of 160 km in the mountainous area of Sanmenxia City, Henan Province, China, serve as the primary research objects and generate several insights. The location and elevation of each power tower pole are determined using an Unmanned Aerial Vehicle (UAV), which assesses the direction and elevation changes in the transmission lines. Moreover, point cloud data of the transmission line corridor are acquired and archived using a UAV equipped with LiDAR during variable-height flight. The data processing of the 3D laser point cloud of the power corridor involves denoising, line repair, thinning, and classification. By calculating the clearance, horizontal, and vertical distances between the power towers, transmission lines, and other surface features, in conjunction with safety distance requirements, information about potential hazards can be generated. The results of detecting these five transmission lines reveal 54 general hazards, 22 major hazards, and an emergency hazard in terms of hazards of the vegetation type. The type of hazard in the current working condition is mainly vegetation, and the types of cross-crossing hazards are power lines and buildings. The detection results are submitted to the local power department in a timely manner, and relevant measures are taken to eliminate hazards and ensure the normal supply of power resources. The research in this paper will provide a basis and an important reference for identifying the potential safety hazards of transmission lines in Henan Province and other complex environments and solving existing problems in the manual inspection of transmission lines. [ABSTRACT FROM AUTHOR]

Objective With years of development, Shanghai urban rail transit is now facing an extremely complex and demanding construction environment. Issues such as traffic congestion, housing demolition, and pipeline relocation caused by large-scale open excavation of underground stations have gradually become the focal points of public concern. In response to the conflicts and contradictions between metro construction and surrounding environment, Shanghai rail transit proposes several underground excavation technologies for soft soil stratum through continuous technological breakthroughs and experimental validation. Method The development history and current application status of underground excavation technology are introduced. Based on the characteristics of Shanghai rail transit construction, the specific connotations and application scenarios of pipe jacking method, freezing method, and pipe roofing method in Shanghai rail transit underground excavation technology are introduced. Furthermore, the research and pilot applications of new underground excavation station construction technologies such as large-section pipe jacking, ultra-long and extra-large-section pipe curtain method, ultra-shallow buried large-section freezing method, and bundled pipe curtain method are explored. Result & Conclusion Shanghai rail transit underground excavation technology primarily include shield tunneling method, pipe jacking method, freezing method, pipe roofing method, and other special underground excavation methods. These techniques are widely applied in various projects such as underground interval engineering, interval connecting passage engineering, station auxiliary structure engineering, and partial station renovation engineering. New underground excavation station construction technology effectively address the unique characteristics and challenges of Shanghai rail transit construction under new conditions.

Copyright of Coal Geology & Exploration is the property of Xian Research Institute of China Coal Research Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Guangdong Architecture Civil Engineering is the property of Guangdong Architecture Civil Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In response to the current challenges of numerous background influencing factors and low detection accuracy in the open railway foreign object detection, a real-time foreign object detection method based on deep learning for open railways in complex environments is proposed. Firstly, the images of foreign objects invading the clearance collected by locomotives during long-term operation are used to create a railway foreign object dataset that fits the current situation. Then, to improve the performance of the target detection algorithm, certain improvements are made to the YOLOv7-tiny network structure. The improved algorithm enhances feature extraction capability and strengthens detection performance. By introducing a Simple, parameter-free Attention Module for convolutional neural network (SimAM) attention mechanism, the representation ability of ConvNets is improved without adding extra parameters. Additionally, drawing on the network structure of the weighted Bi-directional Feature Pyramid Network (BiFPN), the backbone network achieves cross-level feature fusion by adding edges and neck fusion. Subsequently, the feature fusion layer is improved by introducing the GhostNetV2 module, which enhances the fusion capability of different scale features and greatly reduces computational load. Furthermore, the original loss function is replaced with the Normalized Wasserstein Distance (NWD) loss function to enhance the recognition capability of small distant targets. Finally, the proposed algorithm is trained and validated, and compared with other mainstream detection algorithms based on the established railway foreign object dataset. Experimental results show that the proposed algorithm achieves applicability and real-time performance on embedded devices, with high accuracy, improved model performance, and provides precise data support for railway safety assurance. [ABSTRACT FROM AUTHOR]

After apple fruit maturation, the optimal harvest period is short, and the picking robot is expected to improve harvesting efficiency. While it is common for apples to be overlapped and occluded by branches and leaves, which pose challenges to the robot's apple harvesting. Therefore, precise and swift identification and localization of the target fruit is crucial. To this end, this paper proposes a lightweight apple detection method, YOLOv5s-ShuffleNetV2-DWconv-Add, or "YOLOv5s-SDA" for short. The red and green apple datasets in natural environment were collected by a mobile phone, which were divided into four categories: red and green apples that can be directly grasped and cannot be directly grasped, in order to avoid damage to the robotic arm. Different deep learning object detection models were compared, with the YOLOv5s algorithm providing superior recognition performance. To improve harvest efficiency and portability of hardware devices, modifications are made to the YOLOv5s algorithm, replacing the Focus, C3, and Conv structures within the backbone with 3 × 3 Conv structures and ShuffleNetV2, removing SPP and C3 structures; substituting the C3 in the Neck portion with DWConv modules; and replacing two Concat layers in the PANet structure with smaller computational Add layers. Results demonstrate that the model achieved a mAP of 94.6% on the test set, doubled the detection speed, and compressed the model weight to 11.8% of its original value, while maintaining model accuracy. This new method exhibits promising performance in fruit target recognition in natural scenes, providing an effective means of visual acquisition for fruit picking robots. [ABSTRACT FROM AUTHOR]

The precise identification of disease traits in the complex sugarcane planting environment not only effectively prevents the spread and outbreak of common diseases but also allows for the real-time monitoring of nutrient deficiency syndrome at the top of sugarcane, facilitating the supplementation of relevant nutrients to ensure sugarcane quality and yield. This paper proposes a human–machine collaborative sugarcane disease detection method in complex environments. Initially, data on five common sugarcane diseases—brown stripe, rust, ring spot, brown spot, and red rot—as well as two nutrient deficiency conditions—sulfur deficiency and phosphorus deficiency—were collected, totaling 11,364 images and 10 high-definition videos captured by a 4K drone. The data sets were augmented threefold using techniques such as flipping and gamma adjustment to construct a disease data set. Building upon the YOLOv8 framework, the EMA attention mechanism and Focal loss function were added to optimize the model, addressing the complex backgrounds and imbalanced positive and negative samples present in the sugarcane data set. Disease detection models EF-yolov8s, EF-yolov8m, EF-yolov8n, EF-yolov7, and EF-yolov5n were constructed and compared. Subsequently, five basic instance segmentation models of YOLOv8 were used for comparative analysis, validated using nutrient deficiency condition videos, and a human–machine integrated detection model for nutrient deficiency symptoms at the top of sugarcane was constructed. The experimental results demonstrate that our improved EF-yolov8s model outperforms other models, achieving mAP_0.5, precision, recall, and F1 scores of 89.70%, 88.70%, 86.00%, and 88.00%, respectively, highlighting the effectiveness of EF-yolov8s for sugarcane disease detection. Additionally, yolov8s-seg achieves an average precision of 80.30% with a smaller number of parameters, outperforming other models by 5.2%, 1.9%, 2.02%, and 0.92% in terms of mAP_0.5, respectively, effectively detecting nutrient deficiency symptoms and addressing the challenges of sugarcane growth monitoring and disease detection in complex environments using computer vision technology. [ABSTRACT FROM AUTHOR]

Green innovation plays a critical role in attaining carbon neutrality and supporting high-quality economic development in China; however, its promotion remains challenging. Hence, this study uses asymmetric innovation theory and fuzzy-set qualitative comparative analysis (fsQCA) to explore the drivers of green innovation in China's complex regional environments. Using data from 30 Chinese provinces, it investigates the combined effects of factors at the market, institution, and technology levels on regional green innovation. The findings indicate the following. (1) R&D investment is necessary for high-level green innovation, whereas its absence is a necessary condition for low-level green innovation. (2) Three configurations produce high-level green innovation, and these configurations coalesce into demand--regulation--subsidy--technology-driven and competition--technology-driven paths. (3) Four configurations result in low-level green innovation, and the antecedent configurations of high-level and low-level green innovation have an asymmetric relationship. This study adds to the understanding of the causal configurations that promote green innovation and provides valuable insights for policymakers in China and other developing regions. [ABSTRACT FROM AUTHOR]

Copyright of Engineering Mechanics / Gongcheng Lixue is the property of Engineering Mechanics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To solve the complicated steering control of small vehicles in the agriculture and difficult steering on complex roads, this study designed a wheel-track composite vehicle. The vehicle incorporated a novel power differential steering mechanism with dual driving, enabling steering through the differential rotation of the rear two wheels. The vehicle is simple to control, small in size, and is able to work under the conditions of complex roads, such as hills, mountains, and muddy land. The study initially focused on presenting the design, theoretical analysis, and dynamic simulation analysis of the power differential steering mechanism with dual driving. Subsequently, the vehicle underwent modeling and simulation using UG software to validate the reasonability of the values. Finally, utilizing test data, four mathematical models for the actual steering radius of the vehicle on four road surfaces were derived through neural network fitting. The maximum relative error between the model results and the actual steering radius value was reported to be 3.53%. The advantages of the vehicle included continuous radius steering, deceleration and torsion increase, differential lock, etc. This made it well-suited for applications in all-terrain military and civilian vehicles, as well as various special equipment mobile platforms equipped with walking devices. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Plant disease detection remains a significant challenge, necessitating innovative approaches to enhance detection efficiency and accuracy. This study proposes an improved YOLOv8 model, SerpensGate-YOLOv8, specifically designed for plant disease detection tasks. Key enhancements include the incorporation of Dynamic Snake Convolution (DySnakeConv) into the C2F module, which improves the detection of intricate features in complex structures, and the integration of the SPPELAN module, combining Spatial Pyramid Pooling (SPP) and Efficient Local Aggregation Network (ELAN) for superior feature extraction and fusion. Additionally, an innovative Super Token Attention (STA) mechanism was introduced to strengthen global feature modeling during the early stages of the network. The model leverages the PlantDoc dataset, a highly generalizable dataset containing 2,598 images across 13 plant species and 27 classes (17 diseases and 10 healthy categories). With these improvements, the model achieved a Precision of 0.719. Compared to the original YOLOv8, the mean Average Precision (mAP@0.5) improved by 3.3%, demonstrating significant performance gains. The results indicate that SerpensGate-YOLOv8 is a reliable and efficient solution for plant disease detection in real-world agricultural environments.

Nitrate is a macronutrient substantial for plant root and shoot growth, however, the availability of nitrate within soil-based and soilless cultivation environments is not consistently optimal, presenting a significant challenge for plant growth and development. Traditional seed stimulation includes scarification, soaking, hormone application and microbial application but they are all invasive. This study pioneered an experimental approach to address the challenges posed by nutrient deficiency in hydroponic environment by integrating Multigene Genetic Programming (MGGP) with immunological computation algorithms, namely Clonal Selection Algorithm (CSA), Ant Colony Optimization Algorithm (ACOA), and COVID Optimization Algorithm (COVIDOA) in determining the exact optimal time exposure to 2 g hypergravity that can induced the growth of three maize genotypes (PSB 92–97, NSIC CN 302, and NSIC CN 282). Through varying dry seed exposure times to hypergravity (6, 12, and 24 h), labeled models gCSA, gACOA, and gCOVIDOA converged to 20.120 h, 22.466, and 19.700 h, respectively, based on the formulated 2-gene model of root-to-shoot ratio as a function of exposure time. Exposure time between 20 and 24 h increased the root-to-shoot ratio (R/S) by at least a factor of 2.631 and the seedling's dry weight by 13.430 g while between 10 and 15 h of exposure reduced the overall biomass. gACOA-treated seedings exhibited an R/S of 3.732 ± 0.067 having the highest uniformity among the control, gCSA, and gCOVIDOA treatments. gACOA-treated seedlings have healthier root hair compared to unexposed seeds after 14 days and revealed the highest rate of increase in metaxylem, xylem, phloem, and radicle diameters with a factor of 3.651 μm/hr, 1.440 μm/hr, 0.872 μm/hr, and 71.602 μm/hr of exposure in 2 g hypergravity. This study implies that stimulating corn seeds using hypergravity can help lessen the introduction of nutrient fertilizers in the long run which could help in reducing the farm expenses.

Abstract Background The occurrence, development, and outbreak of tea diseases and pests pose a significant challenge to the quality and yield of tea, necessitating prompt identification and control measures. Given the vast array of tea diseases and pests, coupled with the intricacies of the tea planting environment, accurate and rapid diagnosis remains elusive. In addressing this issue, the present study investigates the utilization of transfer learning convolution neural networks for the identification of tea diseases and pests. Our objective is to facilitate the accurate and expeditious detection of diseases and pests affecting the Yunnan Big leaf kind of tea within its complex ecological niche. Results Initially, we gathered 1878 image data encompassing 10 prevalent types of tea diseases and pests from complex environments within tea plantations, compiling a comprehensive dataset. Additionally, we employed data augmentation techniques to enrich the sample diversity. Leveraging the ImageNet pre-trained model, we conducted a comprehensive evaluation and identified the Xception architecture as the most effective model. Notably, the integration of an attention mechanism within the Xeption model did not yield improvements in recognition performance. Subsequently, through transfer learning and the freezing core strategy, we achieved a test accuracy rate of 98.58% and a verification accuracy rate of 98.2310%. Conclusions These outcomes signify a significant stride towards accurate and timely detection, holding promise for enhancing the sustainability and productivity of Yunnan tea. Our findings provide a theoretical foundation and technical guidance for the development of online detection technologies for tea diseases and pests in Yunnan.

Abstract The efficient operation of heating ventilation and air conditioning systems relies on advanced control strategies. However, current control methods are often limited by issues such as uncertain system parameter information and spatial coupling constraints related to the supply rate of the air supply fan. To this end, an energy joint thermal comfort management method for complex environments in multiple regions is proposed. The long-term total cost minimization of the system is established, and then the Lyapunov optimization technology is used to design the distributed control algorithm. Simulation validation shows that the proposed method reduces the energy cost by an average of 11.24% compared to other methods with a thermal discomfort cost coefficient of 0. The average temperature deviation in the area is improved by 0.15 °C and 0.68 °C, respectively. The method saves more than 10% of the total energy cost under different thermal perturbations with an average total temperature deviation of 0.04 °C. The results indicate that the proposed energy joint thermal comfort management method can flexibly balance energy costs and user thermal comfort without knowing any prior information of system parameters, which can also greatly protect user privacy information. This method has application value in the control of heating ventilation and air conditioning systems in complex environments such as commercial buildings.

Background: The occurrence, development, and outbreak of tea diseases and pests pose a significant challenge to the quality and yield of tea, necessitating prompt identification and control measures. Given the vast array of tea diseases and pests, coupled with the intricacies of the tea planting environment, accurate and rapid diagnosis remains elusive. In addressing this issue, the present study investigates the utilization of transfer learning convolution neural networks for the identification of tea diseases and pests. Our objective is to facilitate the accurate and expeditious detection of diseases and pests affecting the Yunnan Big leaf kind of tea within its complex ecological niche. Results: Initially, we gathered 1878 image data encompassing 10 prevalent types of tea diseases and pests from complex environments within tea plantations, compiling a comprehensive dataset. Additionally, we employed data augmentation techniques to enrich the sample diversity. Leveraging the ImageNet pre-trained model, we conducted a comprehensive evaluation and identified the Xception architecture as the most effective model. Notably, the integration of an attention mechanism within the Xeption model did not yield improvements in recognition performance. Subsequently, through transfer learning and the freezing core strategy, we achieved a test accuracy rate of 98.58% and a verification accuracy rate of 98.2310%. Conclusions: These outcomes signify a significant stride towards accurate and timely detection, holding promise for enhancing the sustainability and productivity of Yunnan tea. Our findings provide a theoretical foundation and technical guidance for the development of online detection technologies for tea diseases and pests in Yunnan. [ABSTRACT FROM AUTHOR]

The efficient operation of heating ventilation and air conditioning systems relies on advanced control strategies. However, current control methods are often limited by issues such as uncertain system parameter information and spatial coupling constraints related to the supply rate of the air supply fan. To this end, an energy joint thermal comfort management method for complex environments in multiple regions is proposed. The long-term total cost minimization of the system is established, and then the Lyapunov optimization technology is used to design the distributed control algorithm. Simulation validation shows that the proposed method reduces the energy cost by an average of 11.24% compared to other methods with a thermal discomfort cost coefficient of 0. The average temperature deviation in the area is improved by 0.15 °C and 0.68 °C, respectively. The method saves more than 10% of the total energy cost under different thermal perturbations with an average total temperature deviation of 0.04 °C. The results indicate that the proposed energy joint thermal comfort management method can flexibly balance energy costs and user thermal comfort without knowing any prior information of system parameters, which can also greatly protect user privacy information. This method has application value in the control of heating ventilation and air conditioning systems in complex environments such as commercial buildings. [ABSTRACT FROM AUTHOR]

Marine anchor buoys, as fixed-point profile observation platforms, are highly susceptible to the threat of ship collisions. Installing cameras on buoys can effectively monitor and collect evidence from ships. However, when using a camera to capture images, it is often affected by the continuous shaking of buoys and rainy and foggy weather, resulting in problems such as blurred images and rain and fog occlusion. To address these problems, this paper proposes an improved YOLOv8 algorithm. Firstly, the polarized self-attention (PSA) mechanism is introduced to preserve the high-resolution features of the original deep convolutional neural network and solve the problem of image spatial resolution degradation caused by shaking. Secondly, by introducing the multi-head self-attention (MHSA) mechanism in the neck network, the interference of rain and fog background is weakened, and the feature fusion ability of the network is improved. Finally, in the head network, this model combines additional small object detection heads to improve the accuracy of small object detection. Additionally, to enhance the algorithm's adaptability to camera detection scenarios, this paper simulates scenarios, including shaking blur, rain, and foggy conditions. In the end, numerous comparative experiments on a self-made dataset show that the algorithm proposed in this study achieved 94.2% mAP50 and 73.2% mAP50:95 in various complex environments, which is superior to other advanced object detection algorithms. [ABSTRACT FROM AUTHOR]

Copyright of China Mining Magazine is the property of China Mining Magazine Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Ground Improvement is the property of Journal of Ground Improvement Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

It has been a challenge to obtain accurate detection results in a timely manner when faced with complex and changing surface target detection. Detecting targets on water surfaces in real-time can be challenging due to their rapid movement, small size, and fragmented appearance. In addition, traditional detection methods are often labor-intensive and time-consuming, especially when dealing with large water bodies such as rivers and lakes. This paper presents an improved water surface target detection algorithm that is based on the YOLOv7 (you only look once) model to enhance the performance of water surface target detection. We have enhanced the accuracy and speed of detecting surface targets by making improvements to three key structures: the network aggregation structure, the pyramid pooling structure, and the down-sampling structure. Furthermore, we implemented the model on mobile devices and designed a detection software. The software enables real-time detection through images and videos. The experimental results demonstrate that the improved model outperforms the original YOLOv7 model. It exhibits a 6.4% boost in accuracy, a 4.2% improvement in recall, a 4.1% increase in mAP, a 14.3% reduction in parameter counts, and archives the FPS of 87. The software has the ability to accurately recognize 11 typical targets on the water surface and demonstrates excellent water surface target detection capability. [ABSTRACT FROM AUTHOR]

This paper presents a novel hybrid optimization method to solve the resource allocation problem for multi-target multi-sensor tracking of drones. This hybrid approach, the Improved Prairie Dog Optimization Algorithm (IPDOA) with the Genetic Algorithm (GA), utilizes the strengths of both algorithms to improve the overall optimization performance. The goal is to select a set of sensors based on norms of weighted distances cost function. The norms are the Euclidean distance and the Mahalanobis distance between the drone location and the sensors. The second one depends on the predicted covariance of the tracker. The Extended Kalman Filter (EKF) is used for state estimation with proper clutter and detection models. Since we use Multi-objects to track, the Joint Probability Distribution Function (JPDA) estimates the best measurement values with a preset gating threshold. The goal is to find a sensor or minimum set of sensors that would be enough to generate high-quality tracking based on optimum resource allocation. In the experimentation simulated with Stone Soup, one radar among five radars is selected at every time step of 50-time steps for 200 tracks distributed over 20 different ground truths. The proposed IPDOA provided optimum solutions for this complex problem. The obtained solution is an optimum offline solution that is used to select one or more sensors for any future flights within the vicinity of the 5 radars. Environment and conditions are assumed to be similar in future drone flights within the radars' defined zone. The IPDOA performance was compared with the other 8 metaheuristic optimization algorithms and the testing showed its superiority over those techniques for solving this complex problem. The proposed simulated model can find the most relevant sensor(s) capable of generating the best quality tracks based on weighted distance criteria (Euclidean and Mahalanobis). That would cut down the cost of operating extra sensors and then it would be possible to move them to other vicinity. [ABSTRACT FROM AUTHOR]

Efficient navigation is crucial for intelligent mobile robots in complex environments. This paper introduces an innovative approach that seamlessly integrates advanced machine learning techniques to enhance mobile robot communication and path planning efficiency. Our method combines supervised and unsupervised learning, utilizing spline interpolation to generate smooth paths with minimal directional changes. Experimental validation with a differential drive mobile robot demonstrates exceptional trajectory control efficiency. We also explore Motion Planning Networks (MPNets), a neural planner that processes raw point-cloud data from depth sensors. Our tests demonstrate MPNet's ability to create optimal paths using the Probabilistic Roadmap (PRM) method. We highlight the importance of correctly setting parameters for reliable path planning with MPNet and evaluate the algorithm on various path types. Our experiments confirm that the trajectory control algorithm works effectively, consistently providing precise and efficient trajectory control for the robot. [ABSTRACT FROM AUTHOR]

The YOLOv7-tiny algorithm does not achieve high detection accuracy for crested ibis in rainy environments. Therefore, we developed a rainy day crested ibis target detection algorithm based on YOLOv7-tiny. Firstly, the RainMix method is used to simulate the rainy day shooting data to synthesise a set of ibis dataset which is closer to the real environment. Then, the k-means algorithm is applied to re-cluster the predicted anchor frames to improve the approximation between the predicted and real frames in the output. Finally, an efficient hybrid attention mechanism (E-SEWSA) is developed and integrated into a lightweight efficient layer aggregation network, while a dense residual network reconstruction module is utilised to improve the detection accuracy of the model. In the PAN+FPN structure, the context information fusion capability of the feature aggregation part of the network is enhanced by integrating the CARAFE module instead of the up-sampling module, so as to improve the model detection accuracy. After experimental verification, the algorithm proposed in this paper has better results in rainy day ibis detection. [ABSTRACT FROM AUTHOR]

This article uses ultra‐high performance concrete (UHPC) to repair damaged concrete and analyzes the durability performance of the repaired concrete. Among them, three erosion solutions with a mass concentration of 2.8% NaCl + 0.29% Na2SO4, 5% NaCl, and 5% NaCl + 10% Na2SO4 were designed to soak the concrete specimens for 1 to 3 months. Taking the SWC test group as an example, when the concrete test block undergoes 30 dry wet cycles and the erosion depth is 20 mm, the difference in chloride ion concentration under different diffusion dimensions is not significant. After 90 dry wet cycles, at a depth of 20 mm, the chloride ion concentration inside the concrete in the three‐dimensional diffusion state reached 3 times that of one‐dimensional diffusion, and the chloride ion concentration in the two‐dimensional diffusion state also reached 2 times that of one‐dimensional diffusion. At a depth of 30 mm, a small amount of chloride ion content was also found in the two‐dimensional diffusion and three‐dimensional diffusion. [ABSTRACT FROM AUTHOR]

In this study, our aim is to find an effective method to solve the problem of disease similarity caused by multiple diseases occurring on the same leaf. This study proposes the use of an optimized RegNet model to identify seven common apple leaf diseases. We conducted comparisons and analyses on the impact of various factors, such as training methods, data expansion methods, optimizer selection, image background, and other factors, on model performance. The findings suggest that utilizing offline expansion and transfer learning to fine-tune all layer parameters can enhance the model's classification performance, while complex image backgrounds significantly influence model performance. Additionally, the optimized RegNet network model demonstrates good generalization ability for both datasets, achieving testing accuracies of 93.85% and 99.23%, respectively. These results highlight the potential of the optimized RegNet network model to achieve high-precision identification of different diseases on the same apple leaf under complex field backgrounds. This will be of great significance for intelligent disease identification in apple orchards in the future. [ABSTRACT FROM AUTHOR]

Accurate and stable positioning is significant for vehicle navigation systems, especially in complex urban environments. However, urban canyons and dynamic interference make vehicle sensors prone to disturbance, leading to vehicle positioning errors and even failures. To address these issues, an adaptive loosely coupled IMU/GNSS/LiDAR integrated navigation system based on factor graph optimization with sensor weight optimization and fault detection is proposed. First, the factor nodes and system framework are constructed based on error models of sensors, and the optimization method principle is derived. Second, the interactive multiple-model algorithm based on factor graph optimization (IMMFGO) is utilized to calculate and adjust sensor weights for global optimization, which will reduce the impact of disturbed sensors. Finally, a multi-stage fault detection, isolation, and recovery (MSFDIR) strategy is implemented based on the IMMFGO results and IMU pre-integration measurements, which can detect significant sensor faults and optimize the system structure. Vehicle experiments show that our IMMFGO method generally obtains better performance in positioning accuracy by 23.7% compared to adaptive factor graph optimization (AFGO) methods, and the MSFDIR strategy possesses the capability of fault sensor detection, which provides an essential reference for multi-source vehicle navigation systems in urban canyons. [ABSTRACT FROM AUTHOR]

Mars exploration significantly advances our understanding of planetary evolution, the origin of life, and possibilities for Earth's future. It also holds potential for discovering new mineral resources, energy sources, and potential settlement sites. Navigating Mars' complex environment and unknown terrain is a formidable challenge, particularly for autonomous exploration. The hexapod walking robot, inspired by ant morphology, emerges as a robust solution. This design offers diverse gait options, mechanical redundancy, high fault tolerance, and stability, rendering it well suited for Martian terrain. This paper details the development of an ant-inspired hexapod robot, emphasizing its terrain adaptability on Mars. A novel terrain detection method utilizing a convolutional neural network enables efficient identification of varied terrain types through semantic segmentation of visual images. Additionally, we introduce a comprehensive motion performance evaluation index for the hexapod robot, including speed and stability. These metrics facilitate effective performance assessment in different environments. A key innovation is the proposed gait switching method for the hexapod robot. This approach allows seamless transition between gaits while in motion, enhancing the robot's ability to traverse challenging terrains. The experimental results validate the effectiveness of this method. Utilizing gait switching leads to a significant improvement in robot performance and stability—58.5% and 41.4% better than using tripod and amble gaits, respectively. Compared to single tripod, amble, and wave gaits, the comprehensive motion performance indices of the robot improved by 36.3%, 30.6%, and 41.1%, respectively. This study can provide new ideas and methods for the motion evaluation and adaptive gait switching of multilegged robots in complex terrains. It significantly enhances the mobility and adaptability of such robots in challenging environments, contributing valuable knowledge to the field of planetary exploration robotics. [ABSTRACT FROM AUTHOR]

This paper introduces an airborne object dataset comprising 22,516 images categorizing four classes of airborne objects: airplanes, helicopters, drones, and birds. The dataset was compiled from YouTube-8 M, Anti-UAV, and Ahmed Mohsen's dataset hosted on Roboflow. Videos were sourced from the first two platforms and converted into individual frames, whereas the latter dataset already consisted of images. Following collection, the dataset underwent labelling and annotation processes utilizing Roboflow's annotation tool, resulting in 7,900 annotations per class. Researchers can leverage this dataset to develop and refine algorithms for airborne object detection and tracking, with potential applications spanning military surveillance, border security, and public safety.

Cultivated Cistanche deserticola (known as Rou Cong-Rong) is one kind of herbaceous crop in the arid lands and warm deserts of northwestern China, such as Xinjiang, Inner Mongolia, and Qinghai province. The cash crop is also characterized by its high medicinal and economic value. However, manual picking cannot fully meet the large-scale production at present, due to the time and labor cost. Intellectualized picking has already been the major trend to improve efficiency and labor saving in industrial planting. However, it is difficult to accurately identify and locate cistanche tubulosa during picking, due to the complex environmental factors, such as light, shelter and dense targets. In this study, an improved YOLOv5s model (called YOLOv5s-ESTC) was proposed to increase the recognition accuracy of planted Cistanche deserticola in a complex environment. The lightweight model of object detection was more easily deployed into the mobile terminal and embedded device. Firstly, the backbone network of the YOLOv5s was replaced with the lightweight Eifficientnetv2 using the Fused MBConv module. The performance of the model was optimized to minimize the parameter count, computational complexity, and memory usage. Secondly, the C3STR block was blended in the backbone, due to the integrated C3 and Swin Transformer block. The output feature maps from C3STR were fused in the neck network. The receptive field of Cistanche deserticola was expanded within the Swin transformer layer by layer, in order to leverage the shifted window based on the multi-head selfattention. Therefore, the improved model had effectively captured the subtle features in the image. There was some interaction of texture features between pixel neighborhoods, particularly those associated with the smaller-sized targets. As such, the performance of detection was significantly enhanced for Cistanche deserticola. Lastly, the coordinate attention mechanism was integrated into the network. Meanwhile, the relevant feature of the target channel was strengthened to suppress the invalid background. The ablation experiment was designed to validate the performance of an improved model. The results show that the detection accuracy and precision of the improved YOLOv5-ESTC reached 89.8%, and 92.3%, respectively, which were improved by 1.6 and 2.9 percentage, compared with the original YOLOv5s. The parameters and computational complexity were 5.69× 106 MB and 6.8 GB, respectively, which were reduced by 1.33 MB and 9.1 GB, compared with the original model. The weight of the improved model was 11.9 MB, and the inferred time per image was 8.9 ms, which fully met the real-time detection. A comparative test was designed to verify the detection performance of the improved model. The experimental results show that the detection accuracy of the YOLOv5s-ESTC model was improved by 1.1, 3.0, 10.8, 10.3, 8.5, 1.6, 1.2, 1.4, and 0.5 percent, compared with the mainstream SSD, Faster R-CNN, YOLOv3, YOLOv4, YOLOX, YOLOv5s, YOLOv6s, YOLOv7s, and YOLOv8s models in the same environment, respectively. The improved model accurately detected Cistanche deserticola targets without missing any mistakes, particularly in the complex environment, including small targets, the soil background similar to cistanche tubulosa, and the densely distributed samples. The higher recognition accuracy was achieved in the improved model, compared with the rest using deep learning. The model before and after improvement was also deployed to the mobile terminal device. The YOLOv5s-ESTC model exhibited excellent detection capability on the mobile platform. The detection time was 110.81 ms, indicating a notable improvement of 37.8%, compared with the original model. Moreover, the detection accuracy of the YOLOv5s-ESTC model remained at a high level. In conclusion, the improved model can be expected to effectively detect the Cistanche deserticola targets in natural environments. The finding can provide a valuable reference to develop intelligent harvesting equipment for cistanche cultivation. [ABSTRACT FROM AUTHOR]

Detecting multiple human targets in indoor scenarios using ultra-wideband (UWB) radar usually involves false detection results caused by the secondary reflections, which might reduce the target detection accuracy and cause a more severe deterioration when the number of targets increases. This article proposed a two-step accuracy improvement method for multitarget detection in environments with multiple human targets of more than three and strong secondary reflections by the surroundings, especially the walls. Based on the rough detection results acquired by the modified CA-CFAR (MCA-CFAR) processing, the first step achieves the primary false alarm suppression using a short-window accumulation in the time domain. Then, the second step applies the decision confidence on the detection results from the first step to assess the reliability of results for improved accuracy. The two-step accuracy improvement could thus have a higher accuracy through cascading false alarm suppression. The effectiveness and accuracy of the proposed algorithm are verified based on the experimental results. [ABSTRACT FROM AUTHOR]

The integration of intelligent robots and human intelligence, that is, human-machine collaborative integration, has realized that the mechanical advantages of robots and the advanced cognitive abilities of humans are concentrated in the same working framework, and can work cooperatively in complex environments, thereby improving efficiency. Aiming at complex human-machine interaction scenarios, especially robots in light condition changes, background interference and motion processes, human posture recognition methods based on machine vision and collision avoidance strategies based on machine learning were compared and summarized, the research status and application of various methods were compared in detail, and the development and application of object recognition and collision avoidance methods were discussed based on deep learning. [ABSTRACT FROM AUTHOR]

Abstract This article uses ultra‐high performance concrete (UHPC) to repair damaged concrete and analyzes the durability performance of the repaired concrete. Among them, three erosion solutions with a mass concentration of 2.8% NaCl + 0.29% Na2SO4, 5% NaCl, and 5% NaCl + 10% Na2SO4 were designed to soak the concrete specimens for 1 to 3 months. Taking the SWC test group as an example, when the concrete test block undergoes 30 dry wet cycles and the erosion depth is 20 mm, the difference in chloride ion concentration under different diffusion dimensions is not significant. After 90 dry wet cycles, at a depth of 20 mm, the chloride ion concentration inside the concrete in the three‐dimensional diffusion state reached 3 times that of one‐dimensional diffusion, and the chloride ion concentration in the two‐dimensional diffusion state also reached 2 times that of one‐dimensional diffusion. At a depth of 30 mm, a small amount of chloride ion content was also found in the two‐dimensional diffusion and three‐dimensional diffusion.

Monitoring soil salinity is essential for agricultural development and ecological systems in the coastal saline area. The global navigation satellite system (GNSS) interferometry reflectometry (GNSS-IR) can provide new opportunities to retrieve long-term soil salinity at the point-scale theoretically, because the utilized L-band of GNSS-IR is sensitive to soil dielectric permittivity, while the soil salinity can affect its imaginary part. However, the method of soil salinity retrieval has not been researched currently. This study, taking the GNSS-IR data from the station located in the coastal saline area, gives the first evaluation of using this data source for soil salinity retrievals. First, the three interferogram metrics (i.e., phase, amplitude, and reflector height) and their corresponding statistics are extended to include the contributions from the environmental conditions. Then, the model for semi-empirically retrieving soil salinity from these parameters is constructed through the gradient boosting regression tree (GBRT) machine learning (ML) technique. The results show that the phase and height and their corresponding statistics have a relatively strong relationship with the soil salinity as independent variables. Meanwhile, the soil salinity retrieved from the global positioning system and BeiDou navigation satellite system (BDS) agree and correlate well with the in-situ measurements derived from the 5TE sensor (R varies from 0.670 to 0.808, RMSE varies from 1.350 to 1.895 mS/cm, and MAE ranges from 1.049 to 1.749 mS/cm). The work shows the capability of GNSS-IR in retrieving soil salinity and considerably increases the service available from the geodetic-grade GNSS receivers.

Synthetic Aperture Radar (SAR) finds widespread applications in environmental monitoring, disaster management, ship surveillance, and military intelligence. However, existing target detection methods are ineffective in SAR scenes due to the intricate background environments, small target displays, and irregular appearances. To address these challenges, this thesis introduces a target detection model named YOLOv8-FDF, tailored for SAR scenes based on the YOLOv8 architecture. The model effectively incorporates the FADC module to distinguish targets from complex backgrounds and integrates a deformable feature adaptive mechanism to focus on irregular targets. Additionally, this thesis devised a specialized detection head designed to identify small targets in SAR-wide scenes, thereby improving the effectiveness of detecting such targets. The proposed YOLOv8-FDF model is evaluated on the HRSID dataset. Experiment results show a 3.6% improvement in Map75 on both the training and test sets. Furthermore, under the COCO standard, the model achieves improvements of 4.1%, 2.9%, and 5.5% on AP, AP50, and AP75, along with 6.8%, 1.2%, and 1.2% improvements on small, medium, and large-sized ship detection. An accuracy enhancement of 6.8%, 1.0%, and 14.9% is achieved. These experimental findings validate the efficacy of the proposed YOLOv8-FDF model in SAR scenarios.

Path planning is a critical process in mobile robot navigation. Sampling-based path planning algorithms represented by Rapidly Exploring Random Tree star (RRT*) have gained widespread adoption due to their asymptotic optimality and proven efficiency. However, when applied to intricate environments, characterized by narrow passages and cluttered obstacles, these algorithms encounter challenges in both the initial solution generation and the convergence towards the optimal path, mainly caused by the inefficient sampling strategy, thereby impeding its overall effectiveness. To address these limitations, we introduced Agile-RRT* (A-RRT*), an advancement of RRT* algorithm. Our contributions are twofold: firstly, we introduce an adaptive goal-biased sampling strategy, which employs an adaptive principle for determining the step size on the basis of the goal-biased strategy. This avoids getting trapped in local minima and enhances the efficiency of the initial solution generation. Secondly, we introduce a path optimization approach using a secondary tree and subset-informed sampling, to accelerate the convergence toward the optimal path. It optimizes the path by gradually shrinking the designed elliptical planning space around local states, which effectively narrows down the search space. The experimental results demonstrated that the proposed A-RRT* diminishes the initial solution search time by 71.00% and the sub-optimal solution search time by 82.86% in comparison to RRT*. The A-RRT* exhibits superior performance over RRT*, Informed-RRT*, P-RRT* and Quick-RRT* in terms of soundness and efficiency in narrow and intricate environments. This method could expedite efficient motion planning for drones and mobile robots in complex environments.

Buildings in service are severely affected by the complex environment with multiple coupled factors such as high temperatures, humidity, and inorganic salt attack. In this work, the mechanical properties of nano-SiO2-reinforced geopolymer composites (NSGPC) incorporated with varying dosages of polyvinyl alcohol (PVA) fibers were investigated under a complex environment. A simulated environmental chamber was employed to simulate the complex environment with relative humidity, temperature, and NaCl solution concentration of 100%, 45°C, and 5%, respectively. Fly ash/metakaolin geopolymer composites (GPCs) were fabricated by utilizing 1.5% nano-SiO2 by weight and five various dosages of PVA fibers by volume (0, 0.2, 0.4, 0.6, and 0.8%). The compressive strength, tensile strength, elastic modulus, and impact resistance of NSGPC eroded in a simulated environmental chamber for 60 days were determined. Then, the impact of the PVA fiber dosage on the mechanical properties of NSGPC under complex coupled environments was analyzed. In addition, scanning electron microscopy (SEM) was employed to evaluate and analyze the microstructural behavior of NSGPC under complex environments. Results indicated that the compressive strength, tensile strength, elastic modulus, and impact resistance of NSGPC increased with increasing PVA fiber to 0.6% and then decreased with a continuous increase to 0.8% but remained higher than those of the reference specimen. NSGPC exhibited the best performance at a PVA fiber dosage of 0.6%, which increased by 13.3, 12.0, 17.2, and 522%, respectively. The outcomes of SEM analysis indicated that the usage of PVA fiber and NS remarkably improved the mechanical properties and microstructural behavior of GPC by making the inner structure of GPCs more robust and compact under a complex environment. The outcomes of this work can provide theoretical guidance for buildings serving under a complex environment.