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

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

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

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

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

Accurate identification and monitoring of aircraft on the airport surface can assist managers in rational scheduling and reduce the probability of aircraft conflicts, an important application value for constructing a "smart airport." For the airport surface video monitoring, there are small aircraft targets, aircraft obscuring each other, and affected by different weather, the aircraft target clarity is low, and other complex monitoring problems. In this paper, a lightweight model network for video aircraft recognition in airport field video in complex environments is proposed based on SSD network incorporating coordinate attention mechanism. First, the model designs a lightweight feature extraction network with five feature extraction layers. Each feature extraction layer consists of two modules, Block_A and Block_I. The Block_A module incorporates the coordinate attention mechanism and the channel attention mechanism to improve the detection of obscured aircraft and to enhance the detection of small targets. The Block_I module uses multi-scale feature fusion to extract feature information with rich semantic meaning to enhance the feature extraction capability of the network in complex environments. Then, the designed feature extraction network is applied to the improved SSD detection algorithm, which enhances the recognition accuracy of airport field aircraft in complex environments. It was tested and subjected to ablation experiments under different complex weather conditions. The results show that compared with the Faster R-CNN, SSD, and YOLOv3 models, the detection accuracy of the improved model has been increased by 3.2%, 14.3%, and 10.9%, respectively, and the model parameters have been reduced by 83.9%, 73.1%, and 78.2% respectively. Compared with the YOLOv5 model, the model parameters are reduced by 38.9% when the detection accuracy is close, and the detection speed is increased by 24.4%, reaching 38.2fps, which can well meet the demand for real-time detection of aircraft on airport surfaces. [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. [ABSTRACT FROM AUTHOR]

Gesture recognition nowadays has grabbed the attention of researchers as they represent human behaviour in multiple practical ways. Amongst a variety of gestures available, hand gestures play an essential role in the field of human‐computer interaction when recognised efficiently in complex and dynamic environments. In this paper, we propose a dynamic hand gesture recognition system to recognise hand gestures appearing in different indoor and outdoor environments. Hand detection and tracking uses a two‐level system resulting in the formation of gesture trajectory in challenging conditions in which existing detection and tracking algorithms could not do so. A set of 45 features is provided as input to the various classification techniques. The redundancy problem has been reduced by selecting a set of optimum features using the analysis of variance method, which ranks the list of features. An incremental feature selection technique calculates recognition accuracy by selecting features according to rankings. This system provides an accuracy of 96.32% when used with machine learning and 97.5% when used with deep learning techniques. Recognition accuracy is calculated for various environments, including an extra hand, multiple persons in the video frame, and outdoor environment. All machine‐learning classifiers are combined using classifier combination to calculate the accuracy according to the majority‐voting rule. Based on the experimental results, it has been observed that deep learning provides better results compared to machine learning. [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. [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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

• A dual-branch neural network model is proposed for crop disease recognition. • A new frequency feature extraction network was constructed using the ghost bottleneck module. • A new MASF module has been proposed to fuse frequency and spatial information features. • CV_bias loss function is to reduce the misleading problem in the random prediction process. Crop diseases severely affect crop yield and quality, and accurate identification of crop diseases is crucial for disease management. Although deep neural networks have made progress in the task of crop disease identification, the complex environment of crop diseases, including background interference, morphological differences, and scale variations, has led to limited accuracy in disease recognition. To address these issues, this study proposes a dual-branch deep neural network for crop disease identification, integrating both frequency domain and spatial domain information. The frequency branch takes frequency domain information as input to extract rich crop disease frequency component features, while the deformable attention Transformer branch excels in representing global features and selectively focusing on local features of crop diseases. A new fusion method, Multi-Spectral Channel Attention Fusion (MSAF), is adopted to better integrate crop disease frequency and spatial features. Additionally, an improved bias loss function (cv_bias) is proposed to optimize the dual-branch network model, achieving an accuracy of 96.7 % on the test dataset, which is 2.0 % higher than the existing state-of-the-art deformable attention Transformer model. With only 14 M model parameters, this study's model provides an effective method for future applications in complex environment. [ABSTRACT FROM AUTHOR]

Due to the complex surrounding environment of subway station，subway construction is difficult and has great risk，fuzziness and uncertainty. In order to ensure the safety of subway station construction，a safety evaluation method combining CRITIC method and the approximate ideal sequencing method（TOPSIS）is proposed. Selecting the main factors of personnel，construction，environment and management，a total of 20 three‐level evaluation indexes are established to construct the subway station construction safety evaluation index system. The subway station safety evaluation model is established by using the CRITIC method and TOPSIS. Relying on the Nanchang Rail Transit Line 3 station project，the evaluation model was verified，and the results were consistent with the actual safety status of the station，which verified the practicability，feasibility and accuracy of the model，and thus provide theoretical guidance for similar projects [ABSTRACT FROM AUTHOR]

Accurate simulation of pollution diffusion under uneven terrain with complicated obstacles is a difficult but important scientific and technological issue. The inlet-boundary was important and single boundary meteorological factors (e.g., wind direction and speed) input were generally employed for the Computational Fluid Dynamics (CFD) simulation. However, for uneven terrain with complicated obstacles, the boundary meteorological condition may be quite different, single boundary input may bring large simulation uncertainty. Consequently, the impact of different inlet boundary number and locations on CFD simulation was investigated based on a scarce tracer-gas field experiment carried out in a chemical industrial park located in a mountainous area. The experimental results implied gas diffusion was influenced by a combination of meteorological conditions from multi-inlet boundaries. The simulation results confirmed that and indicated commonly-used single-inlet boundary input can bring great inaccuracies. The case of inlet boundaries closed to emission sources and intersected the crosswind plane agreed best with measurements. It was also found that the inlet boundary number and locations significantly influence velocity and turbulence. Finally, a guideline for determining the inlet boundaries was proposed. This study can enhance the understanding on CFD simulation, and is beneficial for the selection of inlet meteorological sites to precise prediction. [ABSTRACT FROM AUTHOR]

In a complex monitoring environment, GNSS signals, which are extremely susceptible to environmental interference, can produce multipath errors, and the observation data contain a large number of poor observation values, resulting in reduced GNSS monitoring accuracy or even unavailability. Considering that the base station in the monitoring network is usually deployed in an open and unobstructed environment, and the same satellite information tracked by the base station and the monitoring station has a strong correlation, the method of identifying multipath errors in GNSS observation data based on prior information of base station's signal-to-noise ratio(SNR)was proposed. This method utilizes the strong correlation between satellite SNR observations and multipath errors, identifies poor data seriously affected by multipath through making inter-station differences in SNR observations, and removes them to resist the multipath effects of complex monitoring environment. Taking the monitoring environment of one landslide with serious occlusion in Sanmenxia area of Henan as an example, the verification based on the measured data shows that the proposed method can effectively identify the poor observation values with serious multipath effect affected by mountains, vegetation and artificial facilities, has stronger environmental adaptability, and significantly improves the fixed rate of ambiguity and positioning accuracy. Compared with the traditional fixed cut-off(TFC)model and the azimuth-dependent elevation mask(ADEM)model, the proposed method's ambiguity fixed rates are improved by the average of 39.6% and 28.6%, respectively; the positioning accuracy of the fixed solution is better than 4 mm in the E and N directions, and better than 9 mm in the U direction. [ABSTRACT FROM AUTHOR]

Accurate simulation of pollution diffusion under uneven terrain with complicated obstacles is a difficult but important scientific and technological issue. The inlet-boundary was important and single boundary meteorological factors (e.g., wind direction and speed) input were generally employed for the Computational Fluid Dynamics (CFD) simulation. However, for uneven terrain with complicated obstacles, the boundary meteorological condition may be quite different, single boundary input may bring large simulation uncertainty. Consequently, the impact of different inlet boundary number and locations on CFD simulation was investigated based on a scarce tracer-gas field experiment carried out in a chemical industrial park located in a mountainous area. The experimental results implied gas diffusion was influenced by a combination of meteorological conditions from multi-inlet boundaries. The simulation results confirmed that and indicated commonly-used single-inlet boundary input can bring great inaccuracies. The case of inlet boundaries closed to emission sources and intersected the crosswind plane agreed best with measurements. It was also found that the inlet boundary number and locations significantly influence velocity and turbulence. Finally, a guideline for determining the inlet boundaries was proposed. This study can enhance the understanding on CFD simulation, and is beneficial for the selection of inlet meteorological sites to precise prediction. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) systems are required to independently make timely and accurate attack and control strategies according to dynamic battlefield situations. To optimize the trajectory control of UAVs for ground penetration combat, a 3-degree-of-freedom model and a dynamic model of UAV were established. Considering the constraints of initial and terminal conditions and flight performance, the penetration combat model under multiple constraints is established. A dynamic radar cross section (RCS) threat model is demonstrated based on characteristics of RCS. Combined with the hp-adaptive-Radau pseudo-spectral method and the receding horizon control algorithm under the optimal control model, the flight time and the threat probability of the UAV to the combat area were taken as the objective functions. An algorithm to adaptively adjust the weight coefficient of the performance index and switch the sub-objective function was used. The simulation results show that the proposed method can realize autonomous control of UAV attack trajectory under complex battlefield environment and multiple constraints. It can generate UAV flight path with little threat and short time and output relevant state and control parameters. [ABSTRACT FROM AUTHOR]

Substandard discharge of industrial wastewater leads to abundant silver ions (Ag+) emission into water and soil, causing serious environmental threat. Precise and reliable detection of Ag+ is vital for environmental surveillance, while it remains a great challenge in complex actual environment. Herein, a lab-to-factory adoptable photoelectrochemical (PEC) approach for highly selective and sensitive detection of Ag+ in industrial wastewater has been first demonstrated. BiOI nanosheets (NSs) were prepared as photocathode and showed a spontaneous and selective ion-exchange process with Ag+ ions when it immersed into complicated industrial wastewater. The in situ generated AgI/Ag exhibits unique localized surface plasmonic resonance (LSPR) effect to modulate PEC signals for highly selective and sensitive Ag+ detection. As a result, the prepared photocathode delivers an ultralow detection limit (0.21 nM) and a broad linear range (up to 300 μM), outperforming most reported results. This work opens a feasible avenue for Ag+ monitoring in actual environment. • BiOI photocathode can selectively recognize and ion-exchange with Ag+ in complex industrial wastewater. • In situ generated AgI/Ag exhibits unique localized surface plasmonic resonance effect to realize Ag+ detection. • Superior detection property with ultralow detection limit of 0.21 nM and wide linear range up to 300 μM is achieved. • The stable BiOI photocathode show promising potential for practical industrial application. [ABSTRACT FROM AUTHOR]

Automatic fruit recognition is one of the most important steps in fruit picking robots. In this study, a novel fruit recognition was proposed using improved YOLOv3, in order to identify the fruit quickly and accurately for the picking robot in the complex environment of orchard (different light, occlusion, adhesion, large field of view, bagging, whether the fruit was mature or not). The specific procedure was as follows. 1) 4000 Apple images were captured under the complex environment via the orchard shooting and Internet collection. After labeling with LabelImg software, 3200 images were randomly selected as training set, 400 as verification set, and 400 as test set. Mosaic data enhancement was also embedded in the model to improve the input images for the better generalization ability and robustness of model. 2) The network model was also improved. First, the residual module in DarkNet53 network was combined with the CSPNet to reduce the amount of network calculation, while maintaining the detection accuracy. Second, the SPP module was added to the detection network of the original YOLOv3 model, further to fuse the global and local characteristics of fruits, in order to enhance the recall rate of model to the minimal fruit target. Third, a soft NMS was used to replace the traditional for better recognition ability of model, particularly for the overlapping fruits. Forth, the joint loss function using Focal and CIoU Loss was used to optimize the model for a higher accuracy of recognition. 3) The model was finally trained in the deep learning environment of a server, thereby to analyze the training process after the dataset production and network construction. Optimal weights and parameters were achieved, according to the loss curve and various performance indexes of verification set. The results show that the best performance was achieved, when training to the 109th epoch, where the obtained weight in this round was taken as the final model weight, precision was 94.1%, recall was 90.6%, F1 was 92.3%, mean average precision was 96.1%. Then, the test set is used to test the optimal model. The experimental results show that the Mean Average Precision value reached 96.3%, which is higher than 92.5% of the original model; F1 value reached 91.8%, higher than 88% of the original model; The average detection speed of video stream under GPU is 27.8 frame/s, which is higher than 22.2 frame/s of the original model. Furthermore, it was found that the best comprehensive performance was achieved to verify the effectiveness of the improved, compared with four advanced detection of Faster RCNN, RetinaNet, YOLOv5 and CenterNet. A comparison experiment was conducted under different fruit numbers and various lighting environments, further to verify the effectiveness and feasibility of the improved model. Correspondingly, the detection performance of model was significantly better for small target apples and severely occluded overlapping apples, compared with the improved YOLOv3 model, indicating the high effectiveness. In addition, the target detection using deep learning was robust to illumination, where the illumination change presented little impact on the detection performance. Consequently, the excellent detection, robustness and real-time performance can widely be expected to serve as an important support for accurate fruit recognition in complex environment. [ABSTRACT FROM AUTHOR]

In order to study the performance degradation of concrete-filled steel tube under the complex environment of high-altitude coastal areas, 20 concrete-filled steel tubes under axial compression under the combined action of freeze-thaw, salt spray, and freeze-thaw-salt spray were tested to compare different freezing conditions. Axial compression performance of concrete-filled circular steel tube stub columns under melting times and degree of corrosion. The test results show that the axial compression bearing capacity of the concrete-filled circular steel tube short column has a linear decreasing relationship with the degree of action of the three environmental conditions. The combined effect of freeze-thaw and salt spray on the peak load of concrete-filled steel tube is less than the superposition of the freeze-thaw cycle and salt spray corrosion alone. The effect of the three environmental conditions has no obvious relationship with the peak displacement of the specimen, but it will reduce the ductility coefficient. When freeze-thaw and salt spray work together, the constraint coefficient does not change significantly. This is due to the combined effect of the outer steel pipe and the core concrete, which can effectively reduce the performance degradation caused by environmental factors. The proposed calculation formula for the bearing capacity of concrete-filled circular steel tube short columns considering the effects of freeze-thaw cycles and salt spray corrosion is in good agreement with the experimental results. [ABSTRACT FROM AUTHOR]

• Using ER3BP as an accurate model for governing dynamic. • Finding periodic orbits with proper initial guess and correction algorithm. • Coupling orbital and attitude dynamic through SCM. • Searching for periodic attitude behavior of different spacecraft structures along periodic orbit. • Refine full coupled periodic solution in orbit-attitude ER3BP. A way to improve the accuracy of the three-body problem model is taking into account the eccentricity of primary attractors. Elliptic Restricted Three-Body Problem (ER3BP) is a model for studying spacecraft trajectory within the three-body problem such that the orbital eccentricity of primaries is reflected in it. As the principal cause of perturbation in the employed dynamical model, the primaries eccentricity changes the structure of orbits compared to the ideal Circular Restricted Three-Body Problem (CR3BP). It also changes the attitude behavior of a spacecraft revolving along periodic orbits in this regime. In this paper, the coupled orbit-attitude dynamics of a spacecraft in the ER3BP are exploited to find precise periodic solutions as the spacecraft is considered to be in planar orbits around Lagrangian points and Distant Retrograde Orbits (DRO). Periodic solutions are repetitious behaviors in which spacecraft whole dynamics are repeated periodically, these periodic behaviors are the main interest of this study because they are beneficial for future mission designs and allow delineation of the system's governing dynamics. Previous studies laid the foundation for spacecraft stability analysis or studying pitch motion of spacecraft in the ER3BP regime. While in this paper, at first, initial guesses for correction algorithms were derived through verified search methods, then correction algorithms were used to refine calculated orbit-attitude periodic behaviors. Periodic orbits and full periodic solutions are portrayed and compared to previous studies and simpler models. Natural periodic solutions are valuable information eventuate in the longer functional lifetime of spacecraft. Since the problem assumption considered in this paper is much closer to real mission conditions, these results may be the means to use natural bounded motions in the actual operational environment. [ABSTRACT FROM AUTHOR]

With the rapid development in computer vision domain, research on object tracking has directed more attention by scholars. Out of view (OV) is an important challenge often encountered in the tracking process of objects, especially in Internet of Things surveillance. Therefore, this paper proposes a fuzzy-aided solution for OV challenge. This solution uses a fuzzy-aided system to detect whether the target is poorly tracked by using the response matrix of samples. When poor tracking occurs, the target is relocated according to the stored template. The proposed solution is tested on OTB100 dataset, where the experimental results show that the auxiliary solution is effective for the OV challenge. The proposed solution also ensures the tracking speed and overall success rate of visual tracking as well as improves the robustness to a certain extent for IoT-assisted complex environment. [ABSTRACT FROM AUTHOR]

• Simulated corrosion tests of cementitious composites under complex coastal environment were carried out. • Synergistic effect of nano-SiO 2 and PVA fiber on durability of cementitious composites under complex environment was revealed. • Pore structure and size distribution of cementitious composites under complex environment were analyzed. • Microcosmic enhancement mechanism of nano-SiO 2 and PVA fiber was illustrated. As the most commonly used construction material for marine buildings, cementitious composites are exposed to a complex environment of coupled action of relative humidity, temperature, and chloride salts erosion, which severely impacts the safety and longevity of marine buildings. The effect of nano-SiO 2 dosage on the durability of polyvinyl alcohol fiber-reinforced cementitious composites (PVAF-RCC) for the preparation of marine buildings under the complex environment was investigated in this work. The complex environment with the temperature of 50 °C, relative humidity of 100%, and chloride salt concentration of 5% was simulated by the environment chamber. The cementitious composites exposed to the complex environment for 30 d were tested for anti-permeability, anti-chloride ion penetration, frost resistance, and chloride salt erosion resistance. Furthermore, the pore characteristics of the cementitious composites and the micromorphology of nano-SiO 2 , matrix, and fibers were characterized and analyzed by the combination of scanning electron microscope and mercury intrusion porosimetry tests. Results revealed that the durability of cementitious composites improved initially and subsequently decreased with rising the dose of nano-SiO 2 under the complex environment. PVAF-RCC consisting of 1.5% nano-SiO 2 obtained the best durability. As the nano-SiO 2 dosage increased, the total porosity and cumulative porosity of PVAF-RCC decreased first and subsequently increased. Nano-SiO 2 refined and improved the pore structure of PVAF-RCC and the interface between matrix and PVA fibers through its filling effect and participation in the hydration reaction to generate C-S-H gels with filling effect to convert more harmful and harmful pores into less harmful or harmless pores, thus enhancing the compactness and durability of cementitious composites under the complex environment. [ABSTRACT FROM AUTHOR]

The Covid-19 first occurs in Wuhan, China in December 2019. After that, the virus has spread all over the world and at the time of writing this paper the total number of confirmed cases are above 11 million with over 600,000 deaths. The pattern recognition of complex environment can be used to determine if a COVID-19 breath pattern can be established with accuracy. The traditional decorative pattern detection method has a high degree of recognition in simple scene. However, the efficiency of decorative pattern detection in complex scenes is low and the recognition accuracy is not high. Firstly, the evaluation index of target detection method is designed. Through this paper, it is found that the success rate of some targets is naturally better than other targets, and easy to distinguish from the background. In order to improve the recognition success rate of the object in the complex environment and determine the position and attitude of the object, the pattern as the artificial identification in the environment is proposed. The interior art decoration pattern is selected as the experimental pattern and the pattern classification evaluation index is designed. The experimental results show that the method proposed in this paper can optimize the pattern subsets which are confused with each other and easy to distinguish from the background. It has a certain reference value for decorative pattern recognition in complex environment for COVID-19 epidemic. [ABSTRACT FROM AUTHOR]

Macro- and microhabitat preference of Testudo hermanni boettgeri , the eastern subspecies of Hermann's tortoise, was investigated utilizing modified methodology for the western subspecies which emphasized the importance of habitat heterogeneity preservation. The study objective was to explore the habitat preferences of the eastern subspecies of T. hermanni. Research was conducted within the same year at four localities in Eastern and Southeastern Serbia. Macrohabitat determination was conducted using a 0 to 5 land cover score system (coverage with herbaceous, bushy or tree vegetation) for 4 m2 tortoise encounter surroundings. Microhabitat analysis was carried out by determining the plant species in closest contact with the tortoise in the moment of recording. Plants were classified into six groups: 1) aromatic, 2) bramble, 3) herbaceous, 4) thorny shrub, 5) tree and 6) non-thorny shrubs. X 2 test was used for comparison between expected and empirical habitat preference. Results confirmed that the most attractive macrohabitats for Eastern Hermann's tortoises in this part of the Balkans are meadows and open shrublands, with the addition of dense forest (important in wormer months), what is concordant with earlier data from the Mediterranean part of former Yugoslavia. The most attractive microhabitats were "herbaceous plants", followed by "bramble". [ABSTRACT FROM AUTHOR]

The prevailing vehicle recognition technology is adversely affected by the environment such as complex traffic scenarios and weather conditions. This paper proposes a robust vehicle recognition model based on human memory mechanism named Memory-based Vehicle Recognition Model (MVRM). Motivated by the success of memory and attention mechanism, we explore some features of human visual attention model. Fusing short term and long-term memory modules together yield deeper architectures recognizing increasing complex environmental scenarios. Firstly, a rare motion feature has been introduced to measure the visual salience, which improves the accuracy of the visual attention mechanism. Second, a model of vehicle salient region recognition has been established. The results of experiments show that the dynamic vehicle recognition rate of MVRM is 77.10%, while its false recognition rate has only a nominal value of ∼4.5%. Furthermore, the model offers good recognition of vehicle targets under complex environment conditions related to weather and road traffic. [ABSTRACT FROM AUTHOR]

In order to solve the problems of low prediction accuracy and poor real-time prediction in traditional driving risk prediction methods of vehicle, a fuzzy comprehensive evaluation model based driving risk prediction method in complex environment is proposed. In this method, the driving sample data are filtered first, and the incomplete and unstable driving data are removed. The vehicle's driving model is constructed with the processed data, and the force and dynamics of the vehicle's driving model are analyzed with the particle dynamics. The risk factor analysis set is constructed, the entropy weight method is used to determine the risk factor weight, and the five level classification method is used to construct the risk assessment set according to the analysis results of the weight method. The fuzzy evaluation matrix of vehicle's driving risk in complex environment is designed, and the risk prediction model is constructed to realize the prediction of vehicle's driving risk in complex environment. The experimental results show that the prediction accuracy of the proposed method is as high as 0.98, and the prediction is real-time and reliable. [ABSTRACT FROM AUTHOR]

In this study, the power sharing problem of ac microgrids with massive penetration of photovoltaic generators (PVG) is addressed. A robust distributed cooperative control strategy is proposed to control multiple PVGs in ac microgrids under a complex environment (e.g. subject to transmission time delays and noise disturbances). The existing distributed control strategies have been commonly designed assuming ideal communication among PVGs. However, due to inherent communication delays and environmental noises, the real‐life practical channels are affected by time delay and additive noise leading each PVG to receive measurements of the states of its neighbours. Thus, the proposed distributed cooperative control strategy will achieve the accurate power sharing property among PVGs through a sparse communication network subject to time delays and noise disturbances. The theoretical concepts and necessary conditions for stability and robust performance of the proposed distributed cooperative control strategy are outlined by the Lyapunov functional method and stochastic differential equation theory. Furthermore, the proposed control strategy is fully implemented and thus satisfies the plug‐and‐play feature of the future smart grid. Simulation results on an islanded microgrid test system are provided to reveal the effectiveness of the proposed control method to provide accurate proportional power sharing in the MATLAB/SimPowerSystems Toolbox. [ABSTRACT FROM AUTHOR]

In this paper, an end-to-end learning framework—RGB enhanced point cloud fusion network (REPF-Net) is designed based on the complementary and enhanced fusion of multi-sensor (camera and lidar) data, aiming to realize the accurate perception of the robot in a random complex environment. In other words, the three-dimensional position and geometric shape of environmental targets can be obtained excellently and the accuracy of target recognition can be improved. Meanwhile, three key problems in 3D perception tasks were solved including, enhancing the fusion point cloud with RGB feature points, achieving effective fusion of heterogeneous data, and enhancing the skeleton information of the target point set. Point-by-point channel attention network (PCA-Net) is proposed to obtain the deep image features. The loss of attention consistent execution (ACE) loss function is proposed to overcome the inconsistencies of localization and classification confidence. Numerous experiments on the KITTI dataset prove that the proposed REPF-Net is superior to the currently common advanced 3D sensing algorithms. [ABSTRACT FROM AUTHOR]

Highlights • We model pipe wall viscoelasticity during transient waves in a viscoelastic pipe. It is shown that, in the frequency domain, the viscoelastic effect is equivalent to changing the constant wave speed to be frequency-dependent. • Based on this viscoelastic model, matched-field processing (MF) method is applied for pipeline leakage detection. It is a 1D search of leak location along the pipe, independent of leak size. Therefore, it is fast and has a low optimization/computation complexity. • Experimental data at University of Perugia and Hong Kong University of Science and Technology (HKUST) are studied to evaluate the proposed MFP method. It is shown that MFP outperforms three representative methods in the literature (wavelet analysis, frequency response peak pattern, and inverse transient analysis) in the sense of lower localization error. Moreover, MFP leak localization is accurate even for a small leak (the flow ratio of leak and main pipe is approximately 10 • A profound discussion is given to explain the properties of MFP in the aspects of (i) physical complexity modeling; (ii) sufficient use of information from measurements; (iii) multi-sensor information fusion; and (iv) computation complexity. These discussions also guide the choice of data set, experimental setup design, and inverse method for leakage localization. Abstract This paper applies the matched-field processing (MFP) method to leakage localization in a viscoelastic pipe. The viscoelasticity of pipe wall is included in the governing equations of transient wave via the generalized Kelvin-Voigt model and its effect is finally translated into a frequency-dependent wave speed. Then, a leak is localized by MFP via a 1D search of leak location along the pipe, independent of the leak size. Transient experiments with viscoelastic pipe in the Water Engineering Laboratory at University of Perugia and in the Water Resources Research Laboratory at Hong Kong University of Science and Technology are studied. Experimental results demonstrate that the inclusion of pipe wall viscoelasticity and using more frequencies (instead of using only resonant frequencies) improve significantly the leak localization accuracy. It is shown that the MFP leak localization is accurate even for a small leak (the flow ratio of leak and main pipe is approximately 10%) in a noisy environment: among 50 transient experiments, the maximum error of MFP leak localization is only 1.14 m and in the other 49 experiments the error is always lower than 1 m. [ABSTRACT FROM AUTHOR]

The utilization of multiple small-sized automated ground vehicles (AGVs) with omnidirectional movement for cooperative transportation of large objects in manufacturing and logistics is a growing area of research. Utilizing multiple AGVs can improve system flexibility and efficiency compared to using a single large AGV, particularly in clustered and narrow spaces. This study presents a control framework for multiple AGVs to cooperatively transport an object in a cluster environment. The framework utilizes the rapidly-exploring random tree (RRT ∗) algorithm to plan a feasible path through the environment, and a new distributed synchronization control algorithm for each AGV to track its desired trajectory and synchronize with its neighbors. The proposed control framework can be applied in cases with time-varying communication delays and networks with directed spanning tree connections, making it more versatile than existing synchronization control methods. Simulation and experimental results are provided to validate the proposed framework. [ABSTRACT FROM AUTHOR]

How genetic variation is generated and maintained remains a central question in evolutionary biology. When presented with a complex environment, microbes can take advantage of genetic variation to exploit new niches. Here we present a massively parallel experiment where WT and repair-deficient (?mutL) Escherichia coli populations have evolved over 3 y in a spatially heterogeneous and nutritionally complex environment. Metagenomic sequencing revealed that these initially isogenic populations evolved and maintained stable subpopulation structure in just 10 mL of medium for up to 10,000 generations, consisting of up to five major haplotypes with many minor haplotypes. We characterized the genomic, transcriptomic, exometabolomic, and phenotypic differences between clonal isolates, revealing subpopulation structure driven primarily by spatial segregation followed by differential utilization of nutrients. In addition to genes regulating the import and catabolism of nutrients, major polymorphisms of note included insertion elements transposing into fimE (regulator of the type I fimbriae) and upstream of hns (global regulator of environmental-change and stress-response genes), both known to regulate biofilm formation. Interestingly, these genes have also been identified as critical to colonization in uropathogenic E. coli infections. Our findings illustrate the complexity that can arise and persist even in small cultures, raising the possibility that infections may often be promoted by an evolving and complex pathogen population. [ABSTRACT FROM AUTHOR]

The Hamlet test is an innovative device providing a complex environment for testing topographic memory in mice. Animals were trained in groups for weeks in a small village with a central agora, streets expanding from it towards five functionalized houses, where they can drink, eat, hide, run, interact with a stranger mouse. Memory was tested by depriving mice from water or food and analyzing their ability to locate the Drink/Eat house. Exploration and memory were analyzed in different strains, gender, and after different training periods and delays. After 2 weeks training, differences in exploration patterns were observed between strains, but not gender. Neuroanatomical structures activated by training, identified using FosB/ΔFosB immunolabelling, showed an involvement of the hippocampus-subiculum-parahippocampal gyrus axis and dopaminergic structures. Training increased hippocampal neurogenesis (cell proliferation and neuronal maturation) and modified the amnesic efficacy of muscarinic or nicotinic cholinergic antagonists. Moreover, topographical disorientation in Alzheimer's disease was addressed using intracerebroventricular injection of amyloid β 25–35 peptide in trained mice. When retested after 7 days, Aβ 25–35 -treated mice showed memory impairment. The Hamlet test specifically allows analysis of topographical memory in mice, based on complex environment. It offers an innovative tool for various ethological or pharmacological research needs. For instance, it allowed to examine topographical disorientation, a warning sign in Alzheimer's disease. [ABSTRACT FROM AUTHOR]

We analyzed variation in the distribution of eastern Hermann's tortoise (Testudo hermanni boettgeri) in a complex landscape consisting of both natural and human-altered habitats in a temperate climate region in Serbia. Our results indicated nonrandom distribution of tortoises within different habitat types with large and medium effect size for year and activity pattern, respectively. Additionally, the tortoises expressed relatively weak preference for habitats modified by human activity (e.g., vineyards, orchards, or gardens). The results suggested that these tortoises preferred some particular habitats more than others when performing specific activities. They also did not exhibit the same distribution pattern among habitats over consequent years. In other words, they were not randomly distributed among habitat types with regard to specific activity or year. The information on preferences in complex habitat systems is important for the conservation management of eastern Hermann's tortoise and should be considered when planning activities related to sustainable development within the region of study. [ABSTRACT FROM AUTHOR]

Both natural and artificial small-scale swimmers may often self-propel in environments subject to complex geometrical constraints. While most past theoretical work on low-Reynolds number locomotion addressed idealised geometrical situations, not much is known on the motion of swimmers in heterogeneous environments. As a first theoretical model, we investigate numerically the behaviour of a single spherical micro-swimmer located in an infinite, periodic body-centred cubic lattice consisting of rigid inert spheres of the same size as the swimmer. Running a large number of simulations we uncover the phase diagram of possible trajectories as a function of the strength of the swimming actuation and the packing density of the lattice. We then use hydrodynamic theory to rationalise our computational results and show in particular how the far-field nature of the swimmer (pusher versus puller) governs even the behaviour at high volume fractions. [ABSTRACT FROM AUTHOR]

A practical inertial‐measurement‐unit‐based method is proposed to measure the angle of a single‐axis rotation, where the axis rotation is invisible or inaccessible. The proposed method utilises the angular velocities of an inertial‐measurement unit (IMU) to compute the orientation of the rotating axis. An equation with respect to the unknown rotation angle is first developed due to that the attitude angles of IMU vary as the measured target rotates. An interpolation algorithm is proposed to solve the equation, and simulations are performed. Finally, with a turntable and an IMU, comparative experiments are conducted to validate the proposed method. Results demonstrate the robustness to initial noise and the effectiveness under different situations.The proposed method is practical because of its simple structure, flexible installation and applicability in complex environments. Another more contributions of this study are a feasible approach to measure the orientation of the rotating axis. This method solves the problem of complex and difficult installation of angle measuring equipment in specific environments. It is simple and suited for use in the real‐time calculation of angular displacement. It can be used to measure the rotation angle of rotating motors or the rotating axis of some equipment. [ABSTRACT FROM AUTHOR]