Search

Copyright of Chinese Medical Equipment Journal is the property of Chinese Medical Equipment Journal 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.)

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering 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 National University of Defense Technology / Guofang Keji Daxue Xuebao is the property of NUDT Press 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 Geology is the property of Urban Geology 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.)

Spatiotemporal crowdsourcing involves the use of various Internet of Things (IoT) devices distributed across industrial environments to collect and transmit spatiotemporal data related to industrial operations. Unmanned aerial vehicles (UAVs) play a crucial role in further collecting this data from IoT devices, especially in spatiotemporal crowdsourcing tasks. In the realm of industrial IoT energy management, allocating spatiotemporal crowdsourcing resources to UAVs poses substantial challenges. Traditional approaches to this problem have focused on optimizing the Age of Information (AoI) to ensure timely and equitable data updates. However, these methods often overlook critical operational constraints such as UAV no-fly zones and the risk of data interception by eavesdroppers. These issues can adversely affect the freshness and integrity of the information being gathered and transmitted. To address these shortcomings, this paper presents a novel deep reinforcement learning-based framework for UAV spatiotemporal crowdsourcing resource allocation. This approach aims to minimize the average AoI across the network while also reducing the energy consumption of IoT devices. It incorporates spatial constraints imposed by UAV no-fly zones and actively manages the transmission of jamming signals to mitigate the threat posed by eavesdroppers, thus ensuring data security. However, the complexity of allocating spatiotemporal crowdsourcing resources to UAVs is notable owing to numerous decision variables, which increase linearly with the duration of the service. Furthermore, the relationship between performance metrics and decision variables is intricate, requiring adherence to quality of service requirements. This problem is formalized as a Markov decision process (MDP), providing a structured approach to model the decision-making scenario faced by UAVs in a dynamic environment. To solve this MDP, we employ the soft actor critic (SAC) algorithm, an advanced deep reinforcement learning method known for its sample efficiency and stability. The SAC algorithm is adept at handling the continuous action spaces typical of UAV flight paths and power control problems, making it particularly well-suited for our application. We rigorously test our proposed methods in scenarios involving multiple UAVs, demonstrating the algorithm’s effectiveness in managing the spatiotemporal allocation of resources. Our results show that the SAC algorithm achieves faster convergence speed and better solutions than existing state-of-the-art methods, such as the twin delayed deep deterministic policy gradient (TD3) and the deep deterministic policy gradient (DDPG) algorithms. Furthermore, the paper delves into the strategic selection of the optimal number of UAVs to balance the trade-offs between coverage, energy consumption, and operational efficiency. By analytically and empirically examining the impact of the UAV fleet size on system performance, we provide insights into configuring UAV networks to achieve optimal outcomes in terms of AoI, energy management, and security. In conclusion, our research introduces a robust and intelligent framework for UAV resource allocation. The demonstrated efficacy of the SAC algorithm in this context paves the way for its future application in other domains where secure, efficient, and intelligent resource management is paramount.

Aiming at the task assignment problem of heterogeneous UAV performing multi-type fixed tasks on the ground, a task allocation method based on Cooperative Multiple Task Allocation Problem (CMTAP) and Modified Chimp Optimization Algorithm (MChOA) is proposed. Firstly, the CMTAP model is established, and the combat scene is depicted by setting the elements in the quad, and a multi-objective evaluation function with the highest total return, the lowest threat cost, and the shortest total flight range is established. Secondly, the MChOA algorithm is used to complete the model solving: the chaotic reverse learning strategy is adopted to improve the diversity of the intial population distribution. In the iteration stage, the global and local search capabilities of the algorithm are better balanced by adopting the dynamic adjustment strategy of preemptive behavior. Comparative experiments show that compared with the original algorithm, the resulting task allocation scheme reduces the average total voyage in the two scale scenarios by 22.6% and 21.1%, respectively, while maintaining a higher task gain and a lower threat cost.

To address the problem that unmanned aerial vehicle (UAV) face complex time-varying fading channels, which could affect wireless transmission, a joint optimization problem of UAV's trajectory, reconfigurable intelligent surface (RIS) phase shift, offloading slot allocation, CPU frequency allocation, and user equipment transmission power was constructed. In order to solve the constructed problem, the stability constraints of UE and UAV task queues were transformed, and the multi-timeslot stochastic optimization problem was transformed into a deterministic optimization problem for each time slot. A JORL optimization method based on Lyapunov optimization and block coordinate descent (BCD) method was proposed. Firstly, the phase shift of RIS was solved based on the triangle inequality and the closure expression was obtained. Then the technology of transforming the non-convex into the convex problem was used to solve the offloading slot allocation, CPU frequency allocation and user equipment transmission power. Finally, the trajectory of UAV was solved based on successive convex approximation (SCA) method. Simulation results show that JORL has better performance in ensuring queue stability and reducing energy consumption.

With the advancement of the low-altitude economy and the widespread application of unmanned aerial vehicle (UAV), regulatory challenges for UAV flights have significantly increased. Integrated sensing and communication (ISAC) technology is expected to solve the above problems. For this reason, the 3rd Generation Partnership Project (3GPP) had identified low-altitude UAV as one of the important application scenarios of ISAC technology. However, the fast flight speed, small size and high altitude of UAV could adversely affect the strength and quality of the received sensing signals. Consequently, there was an urgent need to investigate the path loss characteristics of the UAV sensing channel and develop effective model. Mono-static sensing channel measurements for UAV at frequencies of 15 GHz and 28 GHz were conducted. The impact of horizontal distance, vertical altitude differences, and the radar cross section (RCS) of the target on path loss were examined. The findings revealed low-coupling characteristics between the target and background channels. Subsequently, the path loss and distance model for the UAV sensing target channel was established, contributing to the ongoing progress of 3GPP standardization.

The low-altitude Internet of things (IoT), based on an air-ground integrated network, combines communication and computing functions. This allows it to efficiently collect, transmit, and analyze data in low-altitude scenarios, continuously empowering the development of the low-altitude economy. In this network, aerial platforms such as unmanned aerial vehicle (UAV) uses onboard sensors to gather multimodal perception data and perform AI-based data processing to support various low-altitude applications, such as agricultural monitoring and environmental modeling. Executing multimodal data inference and content generation tasks requires large AI models. To meet these demands, UAV needs powerful computing resources and vast data support, making efficient model training and optimization essential. However, this poses significant challenges to the current low-altitude IoT network. To address this, an integrated air-ground edge-cloud collaborative framework was proposed, where UAV function as edge nodes, collecting data and performing small-scale computations. Through wireless channels, cloud servers provide large-scale computations and update models for the UAV, enabling efficient collaborations. Given limited wireless communication bandwidth, the framework faces challenges in scheduling information exchange between the UAV and the cloud servers. To solve this, joint optimizations for task allocation, transmission resource management, data quantization, and edge model updates were presented, to improve inference accuracy by maximizing the mean average precision (mAP) of the proposed framework. A closed-form lower bound for the mAP based on the performance of the edge and cloud models were derived and a solution to mAP maximization was proposed. Simulations, based on visual classification experiments, show that the mAP of proposed framework under IoLoUA consistently outperforms centralized and distributed frameworks across various bandwidth and data conditions.

In cell-free massive multiple-input multiple-output (CF-mMIMO) system, unmanned aerial vehicles (UAV), serving as mobile access point (AP), are increasingly playing a significant role in both communication and task execution. To enhance the endurance of UAV during complex mission executions, the energy transmission and trajectory design for UAV within a cell-free mMIMO system were explored, utilizing wireless power transmission (WPT) as the foundation. With a focus on maintaining communication fairness for users experiencing outages, energy consumption constraints as UAV receive power replenishments from access points throughout their missions were considered. Simultaneously, a joint optimization encompassing the UAV's flight trajectory, charging/discharging time slots, and beamforming configurations were conducted. To address this complex problem, an angle search-based communication-assisted deep Q-network (DQN) algorithm was proposed, facilitating a targeted spatial exploration. Simulation results demonstrate that, while balancing endurance and communication requirements, this algorithm significantly elevates the utilization rate of UAV and enhances communication fairness for interrupted user equipment (UE), ultimately achieving dynamic regional coverage.

To exploite reusing a single unmanned aerial vehicle (UAV) to simultaneously act as an aerial base station and anchor point to assist the terrestrial emergency network, a novel integrated air-ground emergency support network was built with joint communication and localization service. The UAV position deployment optimization problem in the integrated network was studied, with a focus on resolving the fundamental and conflicting impacts of UAV deployment to the communication and localization performance. Firstly, a D-optimality based localization accuracy metric was adopted to replace the conventional Cramer-Rao lower bound (CRLB), and a UAV deployment optimization problem that maximizes the network throughput was formulated under individual localization accuracy and communication rate constraints. The closed-form expression of feasible deployment region of a UAV was derived that could satisfy the localization accuracy requirements of ground user, which was shown to be a second-order cone with the target user being the vertex, and proposed efficient deployment algorithm accordingly. Then, a 3D city map assisted deployment scheme was proposed to address the inaccurate air-ground channel modeling problems. The proposed deployment method is analytically tractable for deriving the optimal deployment solution, and has intuitive geometric structure to facilitate fast and performance-guaranteed deployment of integrated air-ground networks to satisfy the communication and localization performance requirements of ground users.

Soil erosion is a major global environmental concern. Safeguarding soil and water resources requires a comprehensive understanding of erosion processes to proactively address and manage soil erosion. Accurately identifying key erosion features and implementing targeted soil and water conservation efforts are therefore imperative. Unmanned Aerial Vehicle (UAV) remote-sensing technology offers a novel method for advancing soil erosion research. As UAV applications in this field continue to progress, it is essential to systematically review and compare recent developments in soil erosion research between China and other countries so as to anticipate future research directions. In this study, we used the CiteSpace software to perform a bibliometric analysis of 871 Chinese and English articles published from 2008 to 2022 that were indexed in the Web of Science core database and China National Knowledge Infrastructure. Using statistical data from these publication databases, CiteSpace, Scimago Graphica, and ArcGIS, visual network maps were generated to summarize the fundamental characteristics and research hotspots related to UAV use in soil erosion research. The results indicate that the overall trend of UAV use in soil erosion studies is increasing. Countries such as the UK, France, Italy, Germany, and Australia have taken the lead, resulting in significant academic influences. Chinese researchers have built on mature research conducted abroad and are actively innovating, resulting in more publications and a growing academic impact. Globally, soil erosion research has predominantly focused on UAV photogrammetric technology and soil erosion mechanisms. Global research hotspots include the acquisition and integration of UAV and multi-source remote sensing data in erosion studies, innovative improvements in UAV-based erosion monitoring and information extraction algorithms, hydrological sediment connectivity and soil erosion model construction using UAV data as a source, and improvements to erosion disaster investigation, prevention, and comprehensive management of soil and water conservation based on UAV remote sensing data. Based on the current state of Chinese and international research, future studies should investigate data processing algorithms, establish precise evaluation and improvement methodologies, and strengthen multi-source and multi-scale data integration to establish the data foundation for understanding sedimentation mechanisms. Additionally, we recommend rationally arranging soil and water conservation measures based on erosion prevention mechanisms, as well as balancing erosion controls, ecological restoration, and sustainable production during the implementation of soil and water conservation efforts.

In response to the issues of multiple operators and significant safety hazards in traditional underwater elevation measurement methods during the monitoring and inspection process of river training works, taking the No.12th dam-No. 20th dam of Shendi river training works as an example, non-manned survey technologies such as unmanned aerial vehicles and unmanned boats were used to simultaneously measure the topography of the river channel and underwater terrain. The slope factor was introduced to improve the regional growth method, and the forest area identified by visual interpretation method was used as a benchmark to analyze and compare the recognition accuracy of the regional growth method and the regional growth method considering slope. The underwater terrain data was interpolated by using the natural neighbor- hood method and fused with the above-water terrain data to obtain the water surface and underwater Digital Elevation Model (DEM) of Shendi river training works, which was compared with the three-dimensional view of the project. The results show that the use of unmanned aerial ve- hicles and unmanned boats for integrated mapping to generate DEM results can meet the requirements of relevant technical specifications and have higher accuracy. The region-growing method considering slope is used to identify the wave-proof forest and roadside forest of the river training works, with a recognition accuracy of 92.47%. [ABSTRACT FROM AUTHOR]

Blast disease has been one of the most serious threats to rice production. However, the conventional evaluation of rice blast resistance cannot fully meet the large-scale production in recent years. In this study, an efficient evaluation was proposed for rice neck blast resistance via combining unmanned aerial vehicle (UAV) low-altitude remote sensing with the YOLOv7 model. 2 565 images of rice neck blast were collected using UAV and then divided into small-sized images (≤ 1240×1240 pixels) in the annotated area. The small-sized images were further subjected to a random combination of five operations, including rotation, scaling, translation, cropping, and changing contrast. The images with low resolution were removed after data cleaning. Finally, the dataset size was expanded with better diversity. The squeeze extinction attention and deformable convolution were introduced into the YOLOv7 model, in order to capture the fine-grain features of the rice neck blast disease spot. YOLOv7_Neckblast model was established for the rice neck blast detection. The number of affected ears was obtained for 15 rice varieties. The incidence rate was calculated for the disease grade of rice neck blast. Among them, 4, 4, 3, 5, 7, and 9 rice varieties of grades 1, 3, 5, and 7, as well as 9, 2, and 2 were assessed by YOLOv7_Neckblast, respectively. At the intersection over the union (IoU) threshold of 0.5, the mean average precision (mAP) of YOLOv7_Neckblast for rice neck blast was 66.4%, which was 4.0, 6.4, and 5.8 percentage points higher than that of the original YOLOv7, FCOS (fully convolutional one-stage object detection), and RetinaNet models, respectively. The recall rate was 75%, which was 4.0, 8.0, and 22.0 percentage points higher than those of the three models, respectively. The F1 score was 71%, which was 4.0, 5.0, and 12.0 percentage points higher than those of the three models, respectively. The training YOLOv7_Neckblast was relatively stable with the low floating-point operations per second (FLOP) under the same training conditions. The loss values remained stable with about 0.019 and 0.020 at the end of training, which was lower than those of FCOS and RetinaNet models. Furthermore, the molecular-assisted marker selection (MAS) showed that the contribution of the Pit and Pib genes to the resistance to the neck blast was 100.0% and 57.14%, respectively. The rice varieties carrying the Pit and Pib genes also exhibited a stronger resistance to disease. In addition, YOLOv7_Neckblast achieved an average accuracy of 86.67% in evaluating 15 rice varieties' resistance, compared with the actual resistance level. The low-altitude UAV remote sensing coupled with machine learning can be used to evaluate the resistance to rice neck blast for rice breeding. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal 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 Integrated Intelligent Energy is the property of Editorial Department of Integrated Intelligent Energy 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 Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics 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 order to solve the security risks caused by UAV “ black flight”, aiming at the problems of large model parameters and long training time of the existing UAV target detection algorithm based on deep learning, a lightweight real-time object detection algorithm for UAV based on YOLOv5 (ED-YOLOv5s) was proposed. Firstly, the feature extraction part of YOLOv5 backbone network was reconstructed by combining the lightweight model efficient model (EMO) . Secondly, normalized gaussian wasserstein distance (NWD) and scale factor were introduced to calculate the similarity between candidate boxes to partially replace IoU. Then, SimAM was introduced to optimize the weight distribution and improved the detection accuracy, so as to achieved the effect of optimizing the Backbone and Head of YOLOv5 network. Finally, the model size is 4. 57 M. ED-YOLOv5s lightweight UAV detection algorithm with floating point computation of 26. 1 (GFLOPs). The experimental data shows that the improved algorithm improves the detection accuracy and realizes the model lightweight. The proposed algorithm achieves 96. 7% AP@ 50 value on the DUT Anti-UAV dataset, and the detection speed reaches 37 frame / s on the RTX2060 GPU. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai University / Shanghai Daxue Xuebao is the property of Journal of Shanghai University (Natural Sciences) 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.)

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications 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 Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications 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 Command Control & Simulation / Zhihui Kongzhi yu Fangzhen is the property of Command Control & Simulation 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.)

Copyright of Journal of Test & Measurement Technology is the property of Publishing Center of North University of China 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 order to solve the problems of IspecHyper multi-rotor UAV hyperspectral imaging system, such as lack of data processing supporting software, large error in collecting multi-band hyperspectral data, lack of coordinates, and inability to automatically stitch. Taking the citrus planting base of Jiaolong Village, Taiping Town, Wuming District as the research area, this paper studies the hyperspectral image processing method of IspecHyper multi-rotor UAV. Firstly, the IspecHyper-VM200 imaging system was used to obtain high-definition photos and multi-band hyperspectral image data of the study area. Secondly, using high-definition photos as data sources, high-resolution UAV orthophotos were formed by PXI4D Mapper software preprocessing and ENVI software image geometric correction. Finally, the ENVI software was used to cut the distorted boundary data of multi-band hyperspectral images, and the geometric correction was completed based on the UAV orthophoto image, and then the hyperspectral reflectance image product was formed through image mosaic and spectral conversion calculation. The experimental results show that the technical method formed in this paper can effectively solve the problems of hyperspectral image processing of IspecHyper multi-rotor UAV, and provide technical reference for hyperspectral image processing of UAV. [ABSTRACT FROM AUTHOR]

Copyright of Tropical Geography is the property of Tropical Geography 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.)

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing 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 Experimental Technology & Management is the property of Experimental Technology & Management 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's proposed that a systematic average age of information optimization algorithm that integrates sensor energy supply, data transmission timeliness, and mobile user requirements. Firstly, an unmanned aerial vehicle assisted WSN is used to secure the energy collection and data transmission from the sensors. Secondly, AoI is introduced as a measure to jointly optimize multi-device scheduling, transmit power and UAV trajectories to establish a non-convex optimization problem with the objective of minimizing the average AoI of the sensors. Then, the non-convex problem is transformed into a convex one by means of constraint relaxation, variable substitution and successive convex approximation, and an iterative average AoI minimization algorithm is designed. Finally, the algorithm is verified through simulation, and the results show that the algorithm effectively improves the freshness of the sensor data while satisfying the mobile user experience. [ABSTRACT FROM AUTHOR]

By analyzing the characteristics of collaborative decision-making behavior of heterogeneous clusters, this paper introduces quantum decision-making model to solve the autonomous decision-making problem of heterogeneous clusters. Firstly, it is triggered from the OODA loop process to clarify the control mode of heterogeneous cluster collaboration. Then, aiming at the specific problem of heterogeneous cluster cooperative cruise, the tasks of unmanned boat cluster and UAV cluster are split, and the application models of formation optimization and autonomous detection are constructed respectively. The formation optimization algorithm is designed to dynamically generate the best communication formation structure. The quantum decision cloud and feedback scoring mechanism are designed to select the most suitable decision content among many possible detection options. Finally, the effectiveness of formation optimization model and autonomous detection model in heterogeneous cluster cooperative cruise is demonstrated by simulation experiments. Experiments show that the quantum decision model has greater applicability and mobility in decision behavior selection and optimization than the conventional decision model.

The northwestern region in China has the characteristics of large coal seam thickness and shallow burial depth. Unmanned aerial vehicle (UAV) thermal infrared monitoring and temperature compensation are key technologies for monitoring shallow coal fire disasters, which is of great significance for advancing the safety monitoring and warning evaluation of coal spontaneous combustion disasters. A temperature compensation model based on the grey wolf optimization-double-layer generalized regression neural network (GWO-double-layer GRNN) and continuous altitude correction function is proposed to address the influence of multiple parameters on thermal infrared temperature monitoring results in complex environments. The factors affecting the temperature results under the thermal infrared monitoring band are selected based on the contrast of the atmospheric extinction coefficient to the received radiation, and the index factors that can cumulatively characterize the actual temperature are determined by principal component analysis. The index factor data from the UAV thermal infrared monitoring experiments in various environments are used as inputs to the GWO-double-layer GRNN network to obtain a discretely trained temperature compensation model. A continuous correction function for UAV altitude is proposed as a pre-processing step for the model input to compensate for the data, and the complete temperature compensation model is tested and field-validated. The results show that the discrete compensation effect of the GWO-double-layer GRNN network is superior to other models in data testing, with MAE ≤ 0.008 1, RMSE ≤ 0.013 2, and R2 ≥ 0.996 9, indicating that the model has good compensation effects. The continuous correction function avoids the influence of UAV altitude on thermal infrared monitoring results, dividing the problem of UAV continuous altitude thermal infrared temperature regression into a stepwise regression calculation, and the final model has a good monitoring accuracy, improving the generalization ability of the temperature compensation model. It provides a supporting calculation method for applying UAV thermal infrared monitoring results to delineate shallow coal fire hazard areas and further promote this method to the corresponding UAV applications and laser monitoring industry.

With the development of marine science and technology, underwater missions have placed greater emphasis on the collaboration of cross-platform systems. However, different application scenarios have varying requirements for clock synchronization. Due to the weak communication and high time delay of underwater communication, a new synchronization method is required for heterogeneous systems involving multiple scenarios. With regard to the problem of global time synchronization under the influence of random time delay, a neural network-based time synchronization algorithm for cross-domain cooperation of unmanned aerial vehicles(UAVs), unmanned surface vessels(USVs), and unmanned undersea vehicles(UUVs) was proposed. First, the impact of random time delay was considered, and the clock of USVs was used as the reference clock. The clock offset of UAVs was corrected through recursive filtering and a neural network. Finally, with the assistance of USVs, the underwater long time delay was estimated by UUVs, and a neural network algorithm was designed to estimate clock drift and clock skew. The effectiveness of the proposed algorithm was verified through simulation.

Purposes This research aims to improve the stability of energy system power variation and fuel economy during flight of hydrogen-hybrid fixed-wing UAV. Methods A variable equivalent factor considering the influence of battery pack power variation amplitude on energy consumption is introduced on the basis of the traditional equivalent consumption minimization strategy theory. This factor is weighted with the original equivalent factor to form a compound equivalent factor. A UAV hybrid simulation platform suitable for complex flight conditions is established, and the influence of compound equivalent factors on the control effect of equivalent consumption minimization strategy is studied. Findings The results show that the equivalent consumption minimization strategy with compound equivalent factor can effectively reduce the power fluctuation amplitude of the energy system. The amplitude of power fluctuation of hydrogen fuel cell during cruise flight is reduced by 94.7% compared with that of the traditional equivalent consumption minimization strategy, and the accumulative SOC variation of battery pack during flight is reduced by 46%. In 3.5 h flight missions, 193 standard liters of hydrogen can be saved, improving fuel economy. When the SOC of the battery pack approaches equilibrium, the change rate of charging power can be greatly slowed down. The improved equivalent consumption minimization strategy is beneficial to prolonging the service life of energy systems and improving the safety of fuel cells and battery packs.

Owing to the increasing complexity of task execution and a wide range of variability in environmental conditions, a single unmanned aerial vehicle (UAV) is insufficient to meet practical mission requirements. Multi-UAV systems have vast potential for applications in areas such as search and rescue. During search and rescue missions, UAVs acquire the location of the target to be rescued and subsequently plan a path that circumvents obstacles and leads to the target. Traditional path-planning algorithms require prior knowledge of obstacle distribution on the map, which may be difficult to obtain in real-world missions. To address the issue of traditional path-planning algorithms that rely on prior map information, this paper proposes a reinforcement learning-based approach for the collaborative exploration of multiple UAVs in unknown environments. First, a Markov decision process is employed to establish a game model and task objectives for the UAV cluster, considering the characteristics of collaborative exploration tasks and various constraints of UAV clusters. To maximize the search and rescue success rate, UAVs must satisfy dynamic and obstacle-avoidance constraints during mission execution. Second, a reinforcement learning-based method for the collaborative exploration of multiple UAVs is proposed. The multiagent soft actor–critic (MASAC) algorithm is used to iteratively train the UAVs’ collaborative exploration strategies. The actor network generates UAV actions, while the critic network evaluates the quality of these strategies. To enhance the algorithm’s generalization capability, training is conducted in randomly generated map environments. To avoid UAVs being obstructed by concave obstacles, a breadth-first search algorithm is used to calculate rewards based on the path distance between the UAVs and targets rather than the linear distance. During the exploration process, each UAV continuously collects and shares the map information with all other UAVs. They make individual action decisions based on the environment and information obtained from other UAVs, and the mission is considered successful if multiple UAVs hover above the target. Finally, a virtual simulation platform for algorithm validation is developed using the Unity game engine. The proposed algorithm is implemented using PyTorch, and bidirectional interaction between the Unity environment and Python algorithm is achieved through the ML-Agents (Machine learning agents) framework. Comparative experiments are conducted on the virtual simulation platform to compare the proposed algorithm with a non-cooperative single-agent SAC algorithm. The proposed method exhibits advantages in terms of task success rate, task completion efficiency, and episode rewards, validating the feasibility and effectiveness of the proposed approach.

In order to improve the performance of aerodynamic and take off and landing from water surface, a new biomimetic UAV with multi-segment wing in ground effect is designed based on the hull fuselage, multi-segment wing and T-tai. The pressure nephogram, pressure coefficient and lift-drag characteristics of the UAV in different states are studied in detail with N-S equation and K-Ω-SST turbulence model. The simulation results show that the influence of ground effect decreases with the increase of the UAV height above water. When the ratio of height above water to mean geometric chord (ratio of height to chord H/c) is close to 1, the ground effect is more significant. When the UAV is 0.2 m above water, the lift coefficient and lift-drag ratio increase by 21.91% and 40.37% respectively, and the drag coefficient decreases by 15.22%. For the multi-segment wing layout, the ground effect flight mainly affects the pressure coefficient and pressure nephogram of the lower surface. And the positive pressure amplitude of pressure coefficient in lower surface gradually decreases from inside to outside along the span direction, and the pressure coefficient near the leading and trailing edges along the chord direction is small. With the pressure nephogram, the results show that the impact of ground effect on the growth rate of lift is mainly concentrated in the area below the mid and inner wing. The lift curve slope can be significantly increased by ground effect (increased by 8.89% when H/c is 1). The growth rate of lift coefficient and the decrease rate of drag coefficient gradually increase when the angle of attack increases, and the growth rate of lift-drag ratio (H/c is 1) reaches more than 26% when the angle of attack is greater than 2°. It has been proven that the design has excellent aerodynamic and flight performance with multiple flight tests of verification UAV taking off and landing from water surface and with and without ground effects. The UAV can provide flight platforms for water quality detection, water surface transportation, search and rescue reconnaissance, etc.

Joint fissures are one of the significant factors that influence the stability of open-pit mine slopes. With advancements in image processing and machine vision technology, the applications of intelligent algorithms for identification have attracted significant attention. Therefore, this paper proposes a method for identifying slope fissures in open-pit mines and deciphering geometric parameters, modernizing the U-net backbone network using residual network (ResNet) series algorithms for fast acquisition of joint fissure geometric information. The high-resolution images of open-pit mine slope fissures are collected using drones by considering factors such as viewpoint, distance, overlap rate, and flight speed. The images are subjected to preprocessing using the global threshold segmentation technique, and data augmentation is performed via random rotation, brightness, and contrast adjustment. The fissure image dataset then undergoes operations such as grayscale, threshold segmentation, dilation, hole filling, and the removal of small connected domain areas to eliminate the influence of background noise. Then, the U-Net network backbone is improved using five types of ResNet models: ResNet 18, 34, 50, 101, and 152. This led to the proposed slope fissure recognition model based on the improved U-net network, which uses the pixel binary classification problem’s accuracy, Intersection over Union (IoU), and F1 Score as evaluation indicators. In addition, the proposed model is trained and assessed using the fissure image dataset. The fissure binary image output is compared with that of traditional fissure recognition methods. The Res101-Unet algorithm achieved accuracy (Pa) and IoU of 96.23% and 62.13%, respectively, offering finer and more extensive fissure recognition results than other methods. Geometric parameter information, such as fissure length and width distribution rules and parameters, is calculated from the fissure binary image. The results show an improvement in the model evaluation indicators owing to the enhancement of the INet model by the ResNet model. Furthermore, the accuracy of the index evaluation increases with the depth of the network layers. The Res101-Unet model reached its highest evaluation index when the number of network layers reached 101, with accuracy, IoU, and F1 scores reaching 95.12%, 60.13%, and 79.53%, respectively. This scenario significantly improves the recognition of simple and complex fissures. As network layers deepen, fissure features can be captured from higher dimensions without substantially increasing network parameters. Thus, comprehensive and structurally distinct fissures can be obtained. The trained Res101-Unet model achieves the highest evaluation index upon reaching 101 network layers. Moreover, the number of recognized fissures on the target slope is consistent with the results obtained using the on-field measuring line method, confirming that the recognition results of this model are consistent with the actual engineering data.

Copyright of China Plastics / Zhongguo Suliao is the property of Journal Office of CHINA PLASTICS 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.)

Barnyard grass is a typical weed in paddy fields that can severely affect the growth and development of rice, ultimately leading to reduced yields. Its appearance is very similar to that of rice, making it difficult to distinguish and posing significant challenges for management. Ideal conditions for barnyard grass identification are achievable in controlled indoor settings but are difficult to replicate in practical applications. For this reason, identifying and mapping barnyard grass in complex paddy field environments holds significant research value and importance. First, hyperspectral images of the paddy fields were captured using UAVs. After image stitching, rectification, and SG (Savitzky-Golay) convolution filtering, a sequential projection algorithm (SPA) was employed to extract sensitive bands for distinguishing rice from barnyard grass. Modeling was performed across the entire spectral range and selected feature bands, employing support vector machines (SVM), random forests (RF), one-dimensional convolutional neural networks (1DCNN), and three-dimensional convolutional neural networks (3DCNN). The results indicated that SPA-3DCNN achieved the best recognition performance for rice (0.9420) and barnyard grass (0.893 6). The seven feature bands selected by SPA (482.523 4, 546.541 5, 675.080 6, 709.138 2, 762.043 1, 922.015 7, and 944.637 1 nm) were valuable for distinguishing barnyard grass from rice. Subsequently, the model was applied to the entire hyperspectral dataset to generate spatial distribution and density maps of barnyard grass. This study successfully explored the feasibility of UAV-based hyperspectral identification of barnyard grass in complex paddy field environments, providing strong data support for the management and prevention of barnyard grass. [ABSTRACT FROM AUTHOR]

Copyright of Acta Agriculturae Zhejiangensis is the property of Acta Agriculturae Zhejiangensis 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.)

Copyright of Journal of Intelligent Agriculture Mechanization is the property of Nanjing Institute of Agricultural Mechanization 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 Command Control & Simulation / Zhihui Kongzhi yu Fangzhen is the property of Command Control & Simulation 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.)

Copyright of Journal of Test & Measurement Technology is the property of Publishing Center of North University of China 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 Telecommunication Engineering is the property of Telecommunication Engineering 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.)

Rodent-infested bald spots are crucial indicators of rodent infestation in grasslands. Leveraging Unmanned Aerial Vehicle (UAV) remote sensing technology for discerning detrimental bald spots among plateau pikas has significant implications for assessing associated ecological hazards. Based on UAV-visible light imagery, we classified and recognized the characteristics of plateau pika habitats with five supervised classification algorithms, i.e., minimum distance classification (MinD), maximum likelihood classification (ML), support vector machine classification (SVM), Mahalanobis distance classification (MD), and neural network classification (NN). The accuracy of the five methods was evaluated using a confusion matrix. Results showed that NN and SVM exhibited superior performance than other methods in identifying and classifying features indicative of plateau pika habitats. The mapping accuracy of NN for grassland and bald spots was 98.1% and 98.5%, respectively, with corresponding user accuracy was 98.8% and 97.7%. The overall model accuracy was 98.3%, with a Kappa coefficient of 0.97, reflecting minimal misclassification and omission errors. Through practical verification, NN exhibited good stability. In conclusion, the neural network method was suitable for identifying rodent-damaged bald spots within alpine meadows. [ABSTRACT FROM AUTHOR]

To address the issue of needing drone aerial photogrammetry in certain projects where there is a lack of basic control points around the survey area, or it is difficult for personnel to enter the area to set up photo control points, this paper proposed a fast aerial survey method for drones without photo control based on global gravity field models, and the accuracy of this method was verified through experiments. The research used three global gravity field models: EGM2008, XGM2019, and EIGEN-6C4, to calculate the height anomalies in six experimental areas and converted the geodetic heights in the image exterior orientation elements to normal heights, which were then input into the aerial survey software for aerial triangulation and 3D modeling. The analysis of the results shows that, the elevation accuracy of the photo control-free aerial survey results based on the EGM2008, XGM2019, and EIGEN-6C4 models is between 10 ~ 20 cm, which meets the requirements of 1:2 000 mapping precision. To further improve the accuracy of the results, this paper adopted a method based on the EIGEN-6C4 gravity field model and the "remove-fit-restore" normal height calculation method, which converted the geodetic heights in the exterior orientation elements of the images obtained by the drone aerial survey system into normal heights. The method calculated the height anomalies of 16 GNSS leveling points across the country, fitted a normal height model for the experimental area, and achieved higher accuracy drone aerial surveys without photo control. The experimental results show that, the elevation accuracy of the photo control-free aerial survey results based on the EIGEN-6C4 model "remove-fit-restore"method can reach the centimeter level, which is higher than the method based only on the EGM2008, XGM2019, and EIGEN-6C4 gravity models, and can meet the requirements of 1:500 mapping precision. [ABSTRACT FROM AUTHOR]

For the three-dimensional path tracking control problem of fixed-wing unmanned aerial vehicles, an inner and outer loop controller based on globally stable integral sliding mode S-plane model is proposed in this paper. The outer loop is controlled by the globally stable integral sliding mode, and the inner loop is controlled by the S-plane. Firstly, the globally stable integral sliding control law is designed for the outer loop, and the stability of the control law is proved using the Lyapunov theory. Then the S-plane controller is designed for the instruction signal of the inner loop. Due to the complexity of derivation in the S-plane controller, a second-order differentiator is introduced. The simulation results show that the proposed controller can track the ideal path accurately, which has good control performance and anti-interference performance. [ABSTRACT FROM AUTHOR]

High-quality and efficient rice production via unmanned construction has been required in modern agriculture. Rice unmanned production can also be accelerated due to the shortage of labor supply at present. In particular, unmanned aerial seeding (UAS) has attracted extensive attention, due to its free of terrain, operating cost-saving, and high efficiency. However, the multiple cropping strategy has been practiced to confine the aerial seed emergence and growth in many rice regions, leading to compression of the rice growth period and a large amount of straw return to the field. This study aims to guarantee the stable and high yield of UAS. The research object was taken as the locally suitable varieties in the early stage. A series of experiments were conducted in the rice-wheat double-maturing region of Jiangsu Province, China. Firstly, rice production was compared between UAS and unmanned carpet-transplanting under straw-return conditions. Subsequently, three seedling treatments were set as 150×104, 195×104 , and 240×104 plants/hm² in UAS. The yield and quality were then evaluated among different population densities. The high-yield cultivation and techniques of UAS rice were summarized on the ecological, growth, and development. The UAS was initially identified with the following ecological characteristics: 1) There was a shortened growth period at low temperatures and light resources; 2) A large amount of straw returned to the field was caused by a low quality of tilled land, resulting in a seedling growth adversity; 3) The economic and ecological costs were reduced with the global warming potential to sustainable development. Besides, the growth and developmental features were obtained: 1) The individual growth was reduced to deteriorate the plant configuration, indicating the ever-increasing lodging risk; 2) There were main stems and tillers forming panicles, while a large wave in the population tiller number; 3) The yield level was restricted to a forward growth center and insufficient accumulation of population dry matter after spiking; 4) There was a deterioration in the rice processing, appearance, and nutritive qualities, whereas, the tasting qualities were improved. Some suggestions were given for the high-yield cultivation approaches. The optimal seedling density was achieved in 195×104 plants/hm² for the UAS conventional japonica rice with both high yield and high quality under straw return. Moreover, the basic seedling and the proportion of main stem panicles were appropriately increasing under the panicle formation, considering both main stems and tillers suitable for cultivation management in favor of the stable and abundant yield of UAS conventional japonica rice. The key technologies of unmanned aerial sowing were also explored for the conventional japonica rice under the straw returning to the field, in terms of variety selection, seed processing, sowing period, tillage and furrow system, planting density, fertilizer and irrigation management, and plant protection. And the prospect for the future application of UAS technology was also proposed: 1) Drone technology can be realized to develop the long duration, lightweight models and new types of seeders; 2) Industry standards can be established to generalize the production of drone accessories; 3) Evaluation can be performed to optimize the distribution of UAS technology, according to regional adaptability. Adequate research can also be conducted on the high-yield and high-quality cultivation approaches. The finding can provide theoretical support for the large-scale application of UAS rice. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Xi'an Jiaotong University is the property of Editorial Office of Journal of Xi'an Jiaotong University 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 Aero Weaponry is the property of Aero Weaponry 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.)

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing 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.)

The existing deep reinforced learning algorithms cannot see local environments and have insufficient perceptual information on UAV autonomous navigation tasks. The paper investigates the UAV's autonomous navigation tasks in its unknown environments based on the nondeterministic policy soft actor-critic (SAC) reinforced learning model. Specifically, the paper proposes a policy network based on a memory enhancement mechanism, which integrates the historical memory information processing with current observations to extract the temporal dependency of the statements so as to enhance the state estimation ability under locally observable conditions and avoid the learning algorithm from falling into a locally optimal solution. In addition, a non-sparse reward function is designed to reduce the challenge of the reinforced learning strategy to converge under sparse reward conditions. Finally, several complex scenarios are trained and validated in the Airsim+UE4 simulation platform. The experimental results show that the proposed method has a navigation success rate 10% higher than that of the benchmark algorithm and that the average flight distance is 21% shorter, which effectively enhances the stability and convergence of the UAV autonomous navigation algorithm.