Search

The indoor environment is typically a complex time-varying environment. At present, the problem of indoor modeling is still a hot research topic for scholars at home and abroad. This paper primarily studies indoor time-varying space. On the basis of the Beidou grid framework and time coding model, in the first scenario, a local space subdivision framework based on Beidou is proposed. The necessity of local space subdivision framework is analyzed. In the second scenario, based on the time coding model needle, a local temporal subdivision model, more suitable for a short time domain, is proposed. Then, for the spatial modeling of an indoor time-varying environment, an indoor time-varying mesh frame based on global subdivision, local space subdivision, and local time subdivision is proposed. Using this framework, the indoor environment is represented by the space–time grid, and the basic storage data structure is designed. Finally, the experiment of local subdivision coding in the indoor space–time grid, indoor space–time grid modeling, and an organization experiment is carried out using real data and simulation data. The experimental results verify the feasibility and correctness of the encoding and decoding algorithm of local subdivision encoding in space–time encoding and the calculation algorithm of the space–time relationship. The experimental results also verify the multi-space organization and the management ability of the indoor space–time grid model. [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.)

The online automated maturity grading and counting of tomato fruits has a certain promoting effect on digital supervision of fruit growth status and unmanned precision operations during the planting process. The traditional grading and counting of tomato fruit maturity is mostly done manually, which is time-consuming and laborious work, and its precision depends on the accuracy of human eye observation. The combination of artificial intelligence and machine vision has to some extent solved this problem. In this work, firstly, a digital camera is used to obtain tomato fruit image datasets, taking into account factors such as occlusion and external light interference. Secondly, based on the tomato maturity grading task requirements, the MHSA attention mechanism is adopted to improve YOLOv8's backbone to enhance the network's ability to extract diverse features. The Precision, Recall, F1-score, and mAP50 of the tomato fruit maturity grading model constructed based on MHSA-YOLOv8 were 0.806, 0.807, 0.806, and 0.864, respectively, which improved the performance of the model with a slight increase in model size. Finally, thanks to the excellent performance of MHSA-YOLOv8, the Precision, Recall, F1-score, and mAP50 of the constructed counting models were 0.990, 0.960, 0.975, and 0.916, respectively. The tomato maturity grading and counting model constructed in this study is not only suitable for online detection but also for offline detection, which greatly helps to improve the harvesting and grading efficiency of tomato growers. The main innovations of this study are summarized as follows: (1) a tomato maturity grading and counting dataset collected from actual production scenarios was constructed; (2) considering the complexity of the environment, this study proposes a new object detection method, MHSA-YOLOv8, and constructs tomato maturity grading models and counting models, respectively; (3) the models constructed in this study are not only suitable for online grading and counting but also for offline grading and counting. [ABSTRACT FROM AUTHOR]

The focus of car-following models is to analyze the microscopic characteristics of traffic flows, with particular attention given to the interaction between adjacent vehicles. This paper presents a systematic review of existing studies on car-following models, with an emphasis on the behavior of both human-driven vehicles (HDVs) and autonomous vehicles (AVs). We classify car-following models based on their applicable background and structure. By considering driving behavior and specific model parameters, we identify the advantages and limitations of each microscopic simulation model in terms of accuracy and continuity. We also discuss model calibration methods, stability analysis, and the impact of complex traffic environments on the car-following process. Finally, we present detailed discussions of each model's features and provide recommendations based on reviewed works and development trends for future research, including mixed traffic flow composed of AVs and HDVs. [ABSTRACT FROM AUTHOR]

Daylily is one of the most favored crops with the great nutritional and economic value. Manual harvesting cannot fully meet the large-scale production at present, due to the inefficient and costly tasks. Mechanized picking can be expected for the large-scale development of daylily industry. However, it is still lacking on the suitable machinery and equipment for the large-scale automated harvesting, due to the complex environments, such as mutual shielding of daylily, interference of weeds, and changes in lighting. It is a high demand for the target identification of daylily under the complex environment for the intelligent harvesting in an automatic harvesting robot. In this study, an improved YOLOv7-MOCA model was established to identify the daylily under the complex environment. The image data of daylily samples were collected from the real harvesting scene in daylily farms. A daylily dataset was established with 12 000 sample images, including 2 000 raw sample image data of single-target, multi-target and occlusion type in sunny and cloudy weather, and 10 000 image data processed by mirroring, brightness transformation, Gaussian filtering, and Gaussian noise processing. Sample images was then enhanced for the better generalization ability of image training models. The daylily dataset in the experiment was divided into a training set, a test set and a validation set, with 7 200, 2 400, and 2 400 images, respectively. The training and identification experiments were then performed on the daylily dataset. The detection effectiveness was compared among the three classical object detection models YOLOv7(You Only Look Once), Faster R-CNN (Fast Region-based Convolutional Network), and SSD (Single Shot MultiBox Detector). The results show that the comprehensive performance of YOLOv7 was better than those of the other two models. As such, an object identification model (YOLOv7-MOCA) was proposed to recognize the daylily in complex environment using YOLOv7. Firstly, a lightweight network model was constructed using MobileOne network as the backbone feature extraction network. Deep convolution was used to replace the ordinary convolution in MobileOne for the less cost of network computing that caused by feature extraction and object detection. The feature extraction efficiency and model detection speed of the model backbone network were compared with the replacement of two mainstream lightweight neural networks. Secondly, the Coordinate Attention (CoordAtt) attention mechanism was integrated in the neck network, in order to improve the receptive field of the model before the convolution operation. The mechanism was enhanced the attention of the model in the characteristics of small target samples of daylily. The comprehensive performance of intelligent identification was achieved in the real-time detection effectiveness of daylily samples. Finally, a YOLOv7-MOCA lightweight daylily fast identification model was designed by ablation test. The MobileOne was used as the backbone network, whereas, a neck network was utilized the attention mechanism of CoordAtt. The experimental results showed that the model YOLOv7-MOCA detection accuracy reached 96.1% with the recall rate of 96.6% and F1 coefficient of 0.96. The model weight was 10MB, whereas, 58 frames per second was used to transmit. Compared with the YOLOv7, the detection accuracy of the improved model increased by 3.2 percentage points, the average detection speed increased by 26.1%, whereas the weight was reduced by 86.7%, indicating the small model weight and fast identification speed. The optimal parameters were obtained from the improved lightweight network model. The detection speed and model weight were significantly redesigned to effectively optimize the identification and detection of daylily in the complex environment of the network model, in terms of the mutual occlusion of target samples, weed interference, and light change. This improved model can be expected to intelligently identify some targets under complex environments for the automatic harvesting equipment of daylily. The finding can provide the technical support for the research of intelligent harvesting equipment of daylily. [ABSTRACT FROM AUTHOR]

Objectives: The complex environmental situation refers to real-time perception services for predicting the environment and its evolutionary trends,which serve as critical information for grasping the situation of actions and analyzing the layout of actions. The real-time perception services of complex environments integrating communication,navigation,and remote sensing fusion constitute a significant area of theoretical research. Methods: Based on the demand for real-time perception services in complex environments, we analyze the innovative ideas of communication, navigation, and remote sensing fusion, outline the scope of perception services for complex environment facilities, construct a real-time perception service platform for complex environments, discuss the architecture of real-time perception services in complex environments, and offer a prospect for complex environment real-time perception services that involve the integration of communication, navigation, and remote sensing fusion. Results: The novel real-time perception service for complex environments fully utilizes communication, navigation, remote sensing information resources, and smart Earth technologies to provide complex environmental factor support for the physical, information, cognitive, and social domain. Conclusions: The real-time perception service for the complex environment of integrated communication, navigation, and remote sensing is a new idea and exploration in the smart earth, and the construction of this system is of great significance. [ABSTRACT FROM AUTHOR]

To improve the precision of intelligent sugarcane harvesting, and to meet the requirements of high precision and low complexity for use with embedded devices, a lightweight model called MobileNet v2-YOLO v5s for the real-time detection of sugarcane stem nodes in complex natural environments was developed. In this study, images of sugarcane stem nodes in a complex natural environment were collected and a dataset containing 12,600 images was constructed using a data extension process. The MobileNet network was introduced to replace the backbone of the YOLO v5s algorithm and the improved algorithm was used to train the MobileNet-YOLO v5s sugarcane stem node identification model. In experiments aiming to verify the advantages of the lightweight model, MobileNet v2-YOLO v5s achieved the best combination of high precision and low complexity. Its AP was decreased by only 0.8%, while its complexity was reduced by 40% compared to YOLO v5s. It also had a fast detection speed of 4.4 ms on a Dell workstation P7920. Therefore, 11 other models were selected for comparative experiments to demonstrate the superiority of MobileNet v2-YOLO v5s. Finally, TensorRT accelerated optimization tests, execution tests, and real-time detection tests were performed on Jetson Nano. The results showed that the optimised MobileNet v2-YOLO v5s outperformed YOLO v5s in terms of identification, lightweight and detection speed on embedded devices. Overall, MobileNet v2-YOLO v5s model meets the requirements of embedded devices and can provide a visual identification method for intelligent sugarcane harvesting.

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.

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.)

Copyright of Railway Construction Technology is the property of Railway Construction Technology 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.)

This study aims to address the challenge of low-cost hardware implementation of a combined backstepping with fractional order PID (FOPID) controller for mobile robots in real-time applications. Moreover, this work proposes a self-designed mobile robot prototype that is easy to realize, low in cost, spares time, and reduces human effort. This robot platform was equipped with two DC motors with quadratic encoders and two passive wheels, controlled by an Arduino mega, where the software code was developed in the Matlab-Simulink environment, using Simulink support package for Arduino. Four case studies were conducted to demonstrate the effectiveness of the suggested methodology. Experimental results demonstrate improved trajectory tracking performance with less tracking error and smooth control efforts, and is capable of handling trajectories with continuous and non-continuous gradients. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Beijing University of Technology is the property of Journal of Beijing University of Technology, 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.)

Abstract The paper presents a novel 2D geometrical path plan algorithm that reduces calculation load and time by filtering obstacles before path planning starts by the newly introduced hexagon filter and also while path search is in progress. Unlike other methods that use only obstacle filtering before path planning starts. The suggested algorithm was able to solve a randomly created maze that contains 400 obstacles with 800 nodes, which was considered before, as next to impossible to solve. This is because computational time is proportional to n2log(n) where n is the number of obstacles, node suggested algorithm reduced the computational time to about 1 in 2500 times compared to the best of (ESOVG, DVG or ECoVG). In addition, the suggested algorithm can be used in the case of a fixed start point and different target points (e.g. a swarm of robots leaving from the same start point with different targets).

A large-section rectangular pipe-jacking tunnel in Shenzhen passed through the bridge pile foundation of an interchange ramp with only a distance of 0.8 m, and down-traversed multi-purpose networks for underground pipelines with only a distance of 3 m. Relying on the tunnel project, the design of the segment of the rectangular pipe-jacking tunnels with large sections and the complex condition was studied in detail. A design flow of the design of the segment of the rectangular pipe-jacking tunnels with large sections and a complex condition was proposed. The design elements of the segment configuration of the rectangular pipe-jacking tunnels with large sections were analyzed. The configuration of the segment was designed. Then three working conditions, including the lifting of the segments, the construction pushing condition, and normal service conditions, were assessed using numerical analysis to determine the dimension of the structures. The joints of the segments, longitudinal connections, reserved holes and so on were specially designed considering the properties of the project with large sections, deep-covered soils and high water pressure. Based on the case investigation, contrastive analysis and practical verification, the paper drew the conclusion that the proposed design flow and the design elements were much helpful for the design of the rectangular pipe-jacking tunnels with large sections in complex condition. [ABSTRACT FROM AUTHOR]

The accurate segmentation of apple leaf disease spots is the key to identifying the classification of apple leaf diseases and disease severity. Therefore, a DeepLabV3+ semantic segmentation network model with an actors spatial pyramid pool module (ASPP) was proposed to achieve effective extraction of apple leaf lesion features and to improve the apple leaf disease recognition and disease severity diagnosis compared with the classical semantic segmentation network models PSPNet and GCNet. In addition, the effects of the learning rate, optimizer, and backbone network on the performance of the DeepLabV3+ network model with the best performance were analyzed. The experimental results show that the mean pixel accuracy (MPA) and mean intersection over union (MIoU) of the model reached 97.26% and 83.85%, respectively. After being deployed into the smartphone platform, the detection time of the detection system was 9s per image for the portable and intelligent diagnostics of apple leaf diseases. The transfer learning method provided the possibility of quickly acquiring a high-performance model under the condition of small datasets. The research results can provide a precise guide for the prevention and precise control of apple diseases in fields. [ABSTRACT FROM AUTHOR]

We propose a hierarchical cognitive navigation model (HCNM) to improve the self-learning and self-adaptive ability of mobile robots in unknown and complex environments. The HCNM model adopts the divide and conquers approach by dividing the path planning task into different levels of sub-tasks in complex environments and solves each sub-task in a smaller state subspace to decrease the state space dimensions. The HCNM model imitates animal asymptotic properties through the study of thermodynamic processes and designs a cognitive learning algorithm to achieve online optimum search strategies. We prove that the learning algorithm designed ensures that the cognitive model can converge to the optimal behavior path with probability one. Robot navigation is studied on the basis of the cognitive process. The experimental results show that the HCNM model has strong adaptability in unknown and environment, and the navigation path is clearer and the convergence time is better. Among them, the convergence time of HCNM model is 25 s, which is 86.5% lower than that of HRLM model. The HCNM model studied in this paper adopts a hierarchical structure, which reduces the learning difficulty and accelerates the learning speed in the unknown environment. [ABSTRACT FROM AUTHOR]

Smart vehicles with embedded Autonomous Vehicle (AV) technologies are currently equipped with different types of mounted sensors, aiming to ensure safe movement for both passengers and other road users. The sensors' ability to capture and gather data to be synchronically interpreted by neural networks for a clear understanding of the surroundings is influenced by lighting conditions, such as natural lighting levels, artificial lighting effects, time of day, and various weather conditions, such as rain, fog, haze, and extreme temperatures. Such changing environmental conditions are also known as complex environments. In addition, the appearance of other road users is varied and relative to the vehicle's perspective; thus, the identification of features in a complex background is still a challenge. This paper presents a pre-processing method using multi-sensorial RGB and thermal camera data. The aim is to handle issues arising from the combined inputs of multiple sensors, such as data registration and value unification. Foreground refinement, followed by a novel statistical anomaly-based feature extraction prior to image fusion, is presented. The results met the AV challenges in CNN's classification. The reduction of the collected data and its variation level was achieved. The unified physical value contributed to the robustness of input data, providing a better perception of the surroundings under varied environmental conditions in mixed datasets for day and night images. The method presented uses fused images, robustly enriched with texture and feature depth and reduced dependency on lighting or environmental conditions, as an input for a CNN. The CNN was capable of extracting and classifying dynamic objects as vehicles and pedestrians from the complex background in both daylight and nightlight images. [ABSTRACT FROM AUTHOR]

Because of the high similarity of leaves of different Cerasus humilis varieties, it is difficult to identify them with the naked eye. In this study, the leaves of four different Cerasus humilis varieties collected in the field were used as the research objects, and a new leaf recognition model based on the improved lightweight convolution neural network model EfficientNet-B0 was proposed. Firstly, the performance of the network models Efficientnet-B0 and ResNet50, GoogleNet, ShuffleNet, and MobileNetV3 were compared based on two different learning methods. Then, the influence of different optimizers on model recognition accuracy was compared based on the optimal model. Finally, different learning rates were used to optimize the optimal model. The results show that the recognition rate of the proposed Efficientnet-B0 +Ranger+0.0005 model was up to 86.9%, which was 2.23% higher than that of the original Efficientnet-B0 model. The results show that this method can effectively improve the recognition accuracy of Cerasus humilis auriculate leaves, which can provide a reference for the deployment of the leaf identification model of Cerasus humilis variety on the mobile terminal. [ABSTRACT FROM AUTHOR]

Swarms of self-propelled micromotors can mimic the processes of natural systems and construct artificial intelligent materials to perform complex collective behaviors. Compared to self-propelled Janus micromotors, the isotropic colloid motors, also called micromotors or microswimmers, have advantages in self-assembly to form micromotor swarms, which are efficient in resistance to external disturbance and the delivery of large quantity of cargos. In this minireview, we summarize the fundamental principles and interactions for the assembly of isotropic active particles to generate micromotor swarms. Recent discoveries based on either catalytic or external physical field-stimulated micromotor swarms are also presented. Then, the strategy for the reconstruction and motion control of micromotor swarms in complex environments, including narrow channels, maze, raised obstacles, and high steps/low gaps, is summarized. Finally, we outline the future directions of micromotor swarms and the remaining challenges and opportunities.

(1) Background: In real road scenarios, various complex environmental conditions may occur, including bright lights, nighttime, rain, and snow. In such a complex environment for detecting pole-shaped obstacles, it is easy to lose the feature information. A high rate of leakage detection, false positives, and measurement errors are generated as a result. (2) Methods: The first part of this paper utilizes the improved YOLOv5 algorithm to detect and classify pole-shaped obstacles. Then, the identified target frame information is combined with binocular stereo matching to obtain more accurate distance information. (3) Results: The experimental results demonstrate that this method achieves a mean average precision (mAP) of 97.4% for detecting pole-shaped obstacles, which is 3.1% higher than the original model. The image inference time is only 1.6 ms, which is 1.8 ms faster than the original algorithm. Additionally, the model size is only 19.0 MB. Furthermore, the range error of this system is less than 7% within the range of 3–15 m. (4) Conclusions: Therefore, the algorithm not only achieves real-time and accurate identification and classification but also ensures precise measurement within a specific range. Meanwhile, the model is lightweight and better suited for deploying sensing systems.

In this paper, considering the heterogeneity of travelers' decision-making behavior caused by travel environment factors, thus affecting the choice of travel path, the theories and methods of travel path choice based on improved cumulative prospect theory (ICPT) in complex environment were proposed. On the basis of cumulative prospect theory (CPT), the value function was improved, and the parameter value range was enlarged. The nonlinear curve of value function and weight function of cumulative prospect theory was fitted through thousands of data tests and experiments. Then according to the decision preference, the decision makers were divided into different categories and the reference point value relationship of heterogeneous decision makers was found. In this paper, fuzzy travel time reference point and periodic dynamic risk degree reference point were set up, and a dynamic path selection model based on heterogeneous double reference point was established to improve the cumulative prospect value. Taking the highway network in Sichuan-Tibet region for example, the optimal path selection scheme of heterogeneous travel groups under the complex environmental factors such as debris flow and landslide in each time stage was studied, and the influence of preference parameter of travel time and risk degree on path choice was analyzed, and then the parameter sensitivity in the cumulative prospect theory (ICPT) was analyzed. The research results verified the rationality of the improved theory and method proposed in this study, which not only provide a new way of thinking for the study of travel path choice in complex environment but also provide theoretical guidance value for supporting regional traffic planning and construction in complex environment. [ABSTRACT FROM AUTHOR]

The paper presents a novel 2D geometrical path plan algorithm that reduces calculation load and time by filtering obstacles before path planning starts by the newly introduced hexagon filter and also while path search is in progress. Unlike other methods that use only obstacle filtering before path planning starts. The suggested algorithm was able to solve a randomly created maze that contains 400 obstacles with 800 nodes, which was considered before, as next to impossible to solve. This is because computational time is proportional to n2log(n) where n is the number of obstacles, node suggested algorithm reduced the computational time to about 1 in 2500 times compared to the best of (ESOVG, DVG or ECoVG). In addition, the suggested algorithm can be used in the case of a fixed start point and different target points (e.g. a swarm of robots leaving from the same start point with different targets). [ABSTRACT FROM AUTHOR]

An accurate and timely monitoring is a high demand for the oestrus behavior of dairy cows in modern dairy farming. In this research, a lightweight recognition was proposed to detect the oestrus behavior of cows using the combined YOLO v5n and channel pruning. The high precision of the network was established before that. Firstly, the model was sparsely trained, according to different sparsity rates. The best sparsity effect of the model was obtained at the sparsity rate of 0.005. Then, the channel pruning was utilized to prune the modules, including the CSPDarknet53 backbone feature extraction network, in order to compress the model structure and parameters for the high detection speed. 2 239 images of cows’ mounting behavior were also collected, including 1 473 images in the daytime, 88 images in the daytime (backlight), and 160 images in the nighttime. Four occlusion situations were considered, including 1 045 images without occlusion, as well as 397, 184, and 95 images with the slight, moderate, and heavy occlusion, respectively. Three target sizes were also considered, including 46, 1 191, and 484 images with the large, medium, and small targets, respectively. The pruned model was veried on the test set, and then to compare with the Faster R-CNN, SSD, YOLOX-Nano, and YOLOv5-Nano. The test results showed that the mean average precision (mAP) of the model after pruning was 97.70%, the Params were 0.72 M, the Floating Point operations (FLOPs) were 0.68 G, and the detection speed was 50.26 fps. Furthermore, the Params and FLOPs were reduced by 59.32%, and 49.63%, respectively, under the condition of constant mAP, and the detection speed increased by 33.71%, compared with the original model YOLOv5-Nano. Consequently, the pruning operation was effectively improved the performance of the model. The mAP of the model was close to the Faster R-CNN, SSD, YOLOX-Nano, but the Params were reduced by 135.97, 22.89, and 0.18 M, respectively, the FLOPs were reduced by 153.69, 86.73, and 0.14 G, respectively, and the detection speed increased by 36.04, 13.22, and 23.02 fps, respectively. In addition, the detection performance of the model was tested in the complex environments, such as different lighting, different occlusion, multi-scale targets, and new environments. The results showed that the mAP was 99.50% in the nighttime environment, the average mAP was 93.53% under the three occlusion conditions, the average mAP was 98.77% under the medium and small targets, the detection rate was 84.62% in the generalization test, and the false detection rate was 7.69%. To sum up, the improved model can fully meet the high requirements for the accurate and real-time monitoring of cows' oestrus behavior under complex breeding environments and all-weather conditions, due to the lightweight, high precision, robust, and high generalization. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Railway Science & Engineering is the property of Journal of Railway Science & 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.)

As a representative of sampling-based planning algorithms, rapidly exploring random tree (RRT), is extensively welcomed in solving robot path planning problems due to its wide application range and easy addition of nonholonomic constraints. However, it is still challenging for RRT to plan the path for configuration space with narrow passages. As a variant algorithm of RRT, rapid random discovery vine (RRV) gives a better solution, but when configuration space contains more obstacles instead of narrow passages, RRV performs slightly worse than RRT. In order to solve these problems, this paper re-examines the role of sampling points in RRT. Firstly, according to the state of the random tree expanding towards the current sampling point, a greedy sampling space reduction strategy is proposed, which decreases the redundant expansion of the random tree in space by dynamically changing the sampling space. Secondly, a new narrow passage judgment method is proposed according to the environment around of sampling point. After the narrow passage is identified, the narrow passage is explored by generating multiple subtrees inside the passage. The subtrees can be merged into the main tree that expands in a larger area by subsequent sampling. These improvements further enhance the value of sampling points. Compared with the existing RRT algorithms, the adaptability for different environments is improved, and the planning time and memory usage are saved. [ABSTRACT FROM AUTHOR]

Storage ponds (storm detention ponds) are an important part of sponge city construction, and the construction process involves deep and large pit excavation in a complex and sensitive environment. Combined with a large storage pond pit engineering design and practice process, in this study, the complex environment of the design of the large storage pond pit method has been explored, using the field measurement data to test the support design of the deformation control effect and to summarize the practice of the key, difficult points of treatment measures. The results show that the procedure adopted in this project is a successful practice for using the pit characteristics, and the special treatment carried out for addressing the difficult points can effectively control the deformation, thus achieving waterproofing and deformation requirements of large-capacity water storage ponds. The design of the support is very effective in controlling the duration and deformation. The cover-cut excavation method, in combination with the support system, is highly effective in deformation control, whereas the open-cut excavation method is more likely to shorten the duration. However, there is an uncertainty in the deformation control during the removal of temporary structures in this method. The deformation characteristics of the pit enclosure and the surrounding ground surface are within the design requirements. The horizontal displacement of the wall and the surrounding settlement are mainly developed during the stage of dismantling the temporary support structure after excavating the first basement level and during the construction of the first and second slabs. The other stages benefit from the strong integrity of the support system, which can efficiently control the deformation. The treatment measures, support types, and component connection methods summarized in this article can provide a reference for the construction of similar underground storage ponds. [ABSTRACT FROM AUTHOR]

The seismic source is one of the key factors for the quality of seismic exploration data acquisition. In complex environments like mountains and rivers, traditional seismic sources are difficult to be applied due to the limitation of road traffic and excitation conditions, which has a negative influence on high-precision seismic exploration. The unmanned aerial vehicle(UAV) remote control seismic source fully takes the advantages of the UAV regardless of the terrain, it can effectively overcome the restrictions of the complex environment. The experiments of the UAV remote source was carried out, including the acquisitive experiments of different excitation energy, comparative experiments with five vibroseis and the acquisitive experiments underwater in Yanghe experimental area in Hebei Province, comparing and analyzing the frequency band and signal to noise radio of the data. The results show that the UAV remote control seismic source has good exploration performance. It has the characteristics of low environmental damage, low energy consumption and easy operation. The UAV remote control seismic source is a seismic source choice with low-cost and high-quality in a complex environment. [ABSTRACT FROM AUTHOR]

Automatic monitoring tools can be useful for assessing the health and welfare status of animals. Specifically, a computer-vision-based tracking tool could be helpful to remotely and automatically monitoring the behaviour of individual animals. However, animals housed in partly covered pens present a particular challenge for animal tracking due to the possibility for animals to disappear from and reappear in the field of view (FOV). The aim of this study was to develop a tracking method for weaner pigs housed in partly covered pens, with the particular aim to re-identify individuals when they reappear in the FOV. In this study a one-shot tracker in which the detection and re-identification (re-ID) branches were jointly trained was adopted for tracking pigs. Three associations based on re-ID features and intersection over union (IoU) were used for matching the correct ID, especially re-identifying individuals reappearing in the FOV. Two sets of short videos were selected to test the model, with a first set of two short videos (mean ± SD: 1m50s ± 20) and a second set of three short videos (mean ± SD: 10m08s ± 3m52s). The model reached the performance of 91.41% and 88.33% in MOTA and IDF1 on the first set of videos, and 81.17% in mean tracking percentage per individual on the second set. The test on one long video (from the same pen, length: 85 m) achieved a tracking percentage of 16.78% per individual. The suggested method improved automatic individual behaviour analysis in complex environments where animals can leave the FOV. • A long-term tracking algorithm was developed for group-housed pigs. • Re-ID features and IoU were used to associate and identify pigs entering the field of view. • An average tracking percentage of 16.78% was obtained on a 85-min-long video. [ABSTRACT FROM AUTHOR]

Purpose: The aim of the article is a Viewpoint based on the General Review of the state of knowledge regarding current trends in project management, taking into account the high volatility of the environment and the increasing complexity of social and technological systems. Design/methodology/approach: The article presents a narrative review of the literature and refers to the results of the author's own research. Findings: With the increasing volatility of the environment and the increasing complexity of social and technological systems, the importance of using hybrid project management and the process approach to management in organisations will increase. Practical implications: Nowadays, a vast majority of organisations manage projects in a traditional way. Observations contained in the article will contribute to the potential attention of decision-makers and the implementation of new management methods in organisations. Social implications: Projects are not only inscribed in the activities of the organisation but also their results affect the quality of life of the population. By improving the efficiency of project management activities, it is possible to significantly affect the quality of people's lives. Originality/value: The article presents the significance of hybrid project management and the process approach in turbulent times of complexity of social and technological systems in a systematic and review way. [ABSTRACT FROM AUTHOR]

The environment of operation and maintenance of locomotives and rolling stocks is complex. Factors such as dust and reflection make point cloud missing, noise and false points, which seriously affect the accuracy of locomotive robot brake shoe image detection. To this end, this paper presents an size measurement algorithm of locomotive brake shoe for locomotive robot based on 3D image. Firstly, it realizes automatic transformation between the template region of interest (ROI) and measurement ROI according to the camera perspective transformation and point cloud registration algorithm, which makes up for the repeated positioning error of the locomotive robot to a certain extent. For the problem of point cloud missing and noise caused by reflection, RANSAC algorithm is used to fit the reference ROI in plane to make up for the loss of precision. The algorithm of automatic filtering of ordered point cloud is used to filter the point cloud between brake disc and brake shoe effectively. For the difficulty of edge discrimination by the false point cloud caused by lock brake and specular reflection of brake disc surface, this paper designs a filtering operator to preliminarily separate the edge of brake shoe and extract the ROI on the side of the brake shoe. Then, according to the imaging characteristics of the brake shoe, the gradient-based edge extraction and tracking algorithm is used to realize the accurate extraction of point cloud boundary of brake shoe and the accurate measurement of brake release gap. Finally, the accurate dimension measurement is realized by using the statistical method of sorting strategy. By the algorithm presented in this paper, accurate measurement can be realized under the condition that slack gap is less than 2mm. The method is used to measure the brake shoe in different shape, position and lighting complex environment, and the Vernier Caliper is used to measure it. The results show that the measuring accuracy of brake shoe wear size is within ±1mm range, and the algorithm has certain robustness.

Abstract In order to research the ability of AGV to cope with the dynamic path planning in mobile single-object dynamic environment, the dynamic path planning methods based on reduced point and non-reduced point were proposed. This paper focuses on the dynamic path planning method of non-reduced point moving targets, adopts the method of combining historical data and real-time data, establishing the NAR neural network prediction model to predict velocity of moving target, and connecting with the A * algorithm, through the example simulation experiment, the results show that the effective success rate of the algorithm reaches 77.5% in the map environment set in this paper.

Under the severe situation of the COVID-19 pandemic, masks cover most of the effective facial features of users, and their head pose changes significantly in a complex environment, which makes the accuracy of head pose estimation in some systems such as safe driving systems and attention detection systems impossible to guarantee. To this end, we propose a powerful four-branch feature selective extraction network (FSEN) structure, in which three branches are used to extract three independent discriminative features of pose angles, and one branch is used to extract composite features corresponding to multiple pose angles. By reducing the dimension of high-dimensional features, our method significantly reduces the amount of computation while improving the estimation accuracy. Our convolution method is an improved spatial channel dynamic convolution (SCDC) that initially enhances the extracted features. Additionally, we embed a regional information exchange network (RIEN) after each convolutional layer in each branch to fully mine the potential semantic correlation between regions from multiple perspectives and learn and fuse this correlation to further enhance feature expression. Finally, we fuse the independent discriminative features of each pose angle and composite features from the three directions of channel, space, and pixel to obtain perfect feature expression for each pose angle, and then obtain the head pose angle. We conducted extensive experiments on the controlled environment datasets and a self-built real complex environment dataset (RCE) and the results showed that our method outperforms state-of-the-art single-modality methods and performs on par with multimodality-based methods. This shows that our network meets the requirements of accurate head-pose estimation in real complex environments such as complex illumination and partial occlusion.

Automatic systems (ASs) can automatically control the work of controlled objects without unattended participation. They have been extensively used in industry, agriculture, automobiles, robots and other fields in recent years. However, the performance of the controller cannot meet the work requirements under complex environmental conditions. Therefore, improving the control performance is one of the difficult problems that automated systems should solve. Sliding-mode variable structure control has the advantages of fast response, insensitivity to uncertainty and interference and easy implementation; thus, it has been extensively used in the field of complex control systems. This article analyses and explains the research status of motors, microgrids, switched systems, aviation guidance, robots, mechanical systems, automobiles and unmanned aerial vehicles (UAVs) and prospects for the application of sliding-mode variable structure control in complex ASs.

• The LMS-VRR scheme has been proposed to multiplex/demultiplex acoustic OAM beams in complex environments. • The acoustic OAM beams with up to 40 channels have been multiplexed/demultiplexed. • The acoustic OAM beams have been multiplexed/demultiplexed over 20 m away from the randomly arranged sound sources. The spiral sound waves carrying orbital angular momentum (OAM) are significantly more efficient in delivering information and energy than the ordinary plane waves. A major bottleneck in exploring the use of acoustic OAM is the excitation and detection of OAM in an arbitrary environment. Here we propose a scheme combining the multichannel least-mean-square (LMS) algorithm and an improved virtual rotating receiver (VRR) method to multiplex and demultiplex acoustic OAM beams in various complex environments. We experimentally demonstrate that the LMS-VRR scheme can excite and detect the OAM beams in indoor and outdoor real-world environments. We use the LMS algorithm to multiplex OAM beams with up to 40 channels at a circular microphone array by compensating the source signals with the equalization filters. On the other hand, the circular microphone array works as a VRR to demultiplex the OAM beams from the rotational Doppler effect. Additional preprocesses are taken to eliminate the plane wave pollution for the VRR method. The acoustic OAM beams have been multiplexed and demultiplexed over 20 m away from the sound sources on a shady lane. Our work has shown practical prospects in covert communication, and shall further unleash the application potential of acoustic OAM beams by removing the restrictions on exciting and detecting OAM in complex environments. [ABSTRACT FROM AUTHOR]

Dynamic area coverage algorithm is often used in multiple unmanned aerial vehicle (multi-UAV) systems to realize searching or monitoring an area of interest (AOI). Improving coverage efficiency is one of the important research issues of dynamic area coverage. In this paper, we propose a distributed dynamic area coverage algorithm based on a reinforcement learning (RL) algorithm to enhance the coverage efficiency in complex application environments. A coverage information fusion based discrete soft actor–critic algorithm (herein FDSAC) is proposed to solve the non-stationary problem of RL algorithm in area coverage task under the communication constraint environment. Considering the equivalence of UAVs in the area coverage tasks, we introduce a multi-UAV cooperative learning method for FDSAC by sharing the strategy and experience of the UAVs to accelerate the learning speed of FDSAC in multi-UAV system. With the FDSAC algorithm, the UAVs can intelligently select the best coverage points and achieve almost optimal coverage point planning for the entire coverage process. In addition, we also provide a coverage point adjustment method based on the distribution of obstacles to enable the proposed algorithm adapt to obstacle environments. The experimental results demonstrate that the proposed algorithm can efficiently complete the area coverage task in complex communication and stochastic control noise environment. The good scalability, environment adaptability, and practicality of our algorithm have been also confirmed in different complex environments of different experimental platforms. [ABSTRACT FROM AUTHOR]

• β-FeOOH/N,P-MXene Schottky-junction is treated using a simple hydrothermal method. • The doping of nitrogen and phosphorus elements not only enhances the photoelectric properties,but also adds the active site. • The FO-NPM200/H 2 O 2 exhibits a consistently high TC removal rate across a wide pH range. • The FO-NPM200 have high degradation stability and recycling performance. Effective separation of photogenerated electrons and holes, as well as providing ample reactive sites, is crucial for enhancing the activity of photocatalytic degradation of pollutants in water. In this study, we have expanded MXene through a simple water swelling method and successfully doped N and P elements into MXene via a hydrothermal process. This modification not only exfoliates the layered structure of MXene, enlarging its specific surface area and providing abundant reactive sites but also endows MXene with enhanced metallic properties. As a result, an N,P-MXene/FeOOH Schottky junction photocatalyst is constructed. Our photocatalytic experiments revealed that the FO-NPM200/H 2 O 2 system performs well under extreme pH conditions and exhibits excellent photocatalytic stability across a wide pH range (1–13), suggesting its outstanding potential for practical applications in complex environments. Under conditions of pH = 7 without the presence of inorganic salt ions, the degradation rate of tetracycline reached 98.1 %, and the photocatalytic reduction rate of the common heavy metal salt Cr(VI) was as high as 99.4 %. We have also unveiled the band structure of FO-NPM200 using UPS and UV–vis DRS. This research paves a new way for the design and construction of Schottky junction photocatalysts. [ABSTRACT FROM AUTHOR]

The indoor environment is typically a complex time-varying environment. At present, the problem of indoor modeling is still a hot research topic for scholars at home and abroad. This paper primarily studies indoor time-varying space. On the basis of the Beidou grid framework and time coding model, in the first scenario, a local space subdivision framework based on Beidou is proposed. The necessity of local space subdivision framework is analyzed. In the second scenario, based on the time coding model needle, a local temporal subdivision model, more suitable for a short time domain, is proposed. Then, for the spatial modeling of an indoor time-varying environment, an indoor time-varying mesh frame based on global subdivision, local space subdivision, and local time subdivision is proposed. Using this framework, the indoor environment is represented by the space–time grid, and the basic storage data structure is designed. Finally, the experiment of local subdivision coding in the indoor space–time grid, indoor space–time grid modeling, and an organization experiment is carried out using real data and simulation data. The experimental results verify the feasibility and correctness of the encoding and decoding algorithm of local subdivision encoding in space–time encoding and the calculation algorithm of the space–time relationship. The experimental results also verify the multi-space organization and the management ability of the indoor space–time grid model.

The online automated maturity grading and counting of tomato fruits has a certain promoting effect on digital supervision of fruit growth status and unmanned precision operations during the planting process. The traditional grading and counting of tomato fruit maturity is mostly done manually, which is time-consuming and laborious work, and its precision depends on the accuracy of human eye observation. The combination of artificial intelligence and machine vision has to some extent solved this problem. In this work, firstly, a digital camera is used to obtain tomato fruit image datasets, taking into account factors such as occlusion and external light interference. Secondly, based on the tomato maturity grading task requirements, the MHSA attention mechanism is adopted to improve YOLOv8’s backbone to enhance the network’s ability to extract diverse features. The Precision, Recall, F1-score, and mAP50 of the tomato fruit maturity grading model constructed based on MHSA-YOLOv8 were 0.806, 0.807, 0.806, and 0.864, respectively, which improved the performance of the model with a slight increase in model size. Finally, thanks to the excellent performance of MHSA-YOLOv8, the Precision, Recall, F1-score, and mAP50 of the constructed counting models were 0.990, 0.960, 0.975, and 0.916, respectively. The tomato maturity grading and counting model constructed in this study is not only suitable for online detection but also for offline detection, which greatly helps to improve the harvesting and grading efficiency of tomato growers. The main innovations of this study are summarized as follows: (1) a tomato maturity grading and counting dataset collected from actual production scenarios was constructed; (2) considering the complexity of the environment, this study proposes a new object detection method, MHSA-YOLOv8, and constructs tomato maturity grading models and counting models, respectively; (3) the models constructed in this study are not only suitable for online grading and counting but also for offline grading and counting.

In order to solve the problems of low learning efficiency and slow convergence speed when mobile robot uses reinforcement learning method for path planning in complex environment, a reinforcement learning method based on each round path planning result is proposed. Firstly, the algorithm adds obstacle learning matrix to improve the success rate of path planning; and introduces heuristic reward to speed up the learning process by reducing the search space; then proposes a method of dynamically adjusting the exploration factor to balance the exploration and utilization in path planning, so as to further improve the performance of the algorithm. Finally, the simulation experiment in grid environment shows that compared with Q-learning algorithm, the improved algorithm not only shortens the average path length of the robot to reach the target position, but also speeds up the learning efficiency of the algorithm, so that the robot can find the optimal path more quickly. The code of EPRQL algorithm proposed in this paper has been published to GitHub: https://github.com/panpanpanguoguoqian/mypaper1.git. [ABSTRACT FROM AUTHOR]

How to generate the path planning of mobile robots quickly is a problem in the field of robotics. The Q-learning(QL) algorithm has recently become increasingly used in the field of mobile robot path planning. However, its selection policy is blind in most cases in the early search process, which slows down the convergence of optimal solutions, especially in a complex environment. Therefore, in this paper, we propose a continuous local search Q-Learning (CLSQL) algorithm to solve these problems and ensure the quality of the planned path. First, the global environment is gradually divided into independent local environments. Then, the intermediate points are searched in each local environment with prior knowledge. After that, the search between each intermediate point is realized to reach the destination point. At last, by comparing other RL-based algorithms, the proposed method improves the convergence speed and computation time while ensuring the optimal path. [ABSTRACT FROM AUTHOR]

Aiming at the problems of license plate location difficulty and low character recognition accuracy in complex environments, such as a small number of license plate samples, illumination transformation, changeable weather and motion blur, this paper proposes an end-to-end license plate recognition method to improve the location and recognition accuracy in complex environments. First, the cyclic generative adversarial network is used to synthesize the approximate real license plate image to enrich the training set and solve the problem of data imbalance to facilitate subsequent model training. Second, a MF-RepUnet license plate location method is proposed, which integrates the improved VGG structure and feature pyramid into the U-Net model to improve the feature extraction capability of the network, and effectively solve the problem of missing detection of inclined license plate and small-scale license plate. Finally, the convolutional recurrent neural network is improved to accurately predict the feature sequence through the way of attention mechanism weighting, which solves the problem of blurred semantic structure sequence features caused by image degradation and further improves the accuracy of license plate character recognition. Experiments show that the proposed method can effectively improve the accuracy and efficiency of license plate location and character recognition, and can be applied to license plate recognition in various complex environments. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Geodesy & Geodynamics (1671-5942) is the property of Editorial Board Journal of Geodesy & Geodynamics 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.)

Autonomous robots have the potential to play a critical role in urban search and rescue (USAR) by allowing human counterparts of a response team to remain in remote, stable locations while the robots execute more dangerous work in the field. However, challenges remain in developing robot capabilities suitable for teaming with humans. Communicating effectively is one of these challenges, especially if plan deviations during field operations require robot explanation. A virtual USAR team task experiment was conducted in Minecraft with a confederate acting as the remote robot. Four explanation-based communication conditions were tested: (1) always explain–the robot automatically provided explanations for any off-plan behaviors, (2) explain if asked–the robot provided an explanation only when the human counterpart requests it, (3) pull prime–the same as (2) but participants also experienced implicit training to pull information from the robot, and (4) never explain–a baseline condition in which the robot acknowledged requests but would not provide an explanation. Results indicate that the training in (3) generated more team communication than (1), but this did not improve team performance or shared situation awareness. Rather, team performance and shared situation awareness was best supported by a moderate level of explanations and the robot pushing information. These findings reinforce the importance of designing robot communication strategies that can reduce human workload, particularly communication overhead, in dynamic and time-constrained tasks. [ABSTRACT FROM AUTHOR]