摘要 ：

From the perspective of optimizing maritime logistics, a key focus in the field of maritime information research has been how to extract behavioral patterns and deep behavioral characteristics of vessels from vast amounts of shipping statistics. Additionally, aligning these characteristics with infrastructure such as berths for effective association and recommendation to vessels is a critical requirement for the evolution of intelligent maritime systems. Traditional methods primarily focus on the behavioral trajectories of vessel navigation, failing to explore the geographical interconnections between vessels and port infrastructure. In light of this, this paper proposes a framework for deep mining of shipping information based on knowledge graph technology. Utilizing AIS data and spatial data of port facilities, it constructs a semantic relationship in the form of triplets between vessels, berths, and waterways, and semantically models vessel behaviors. Effective identification of vessels is achieved based on various semantic information. Simultaneously, based on the berthing semantic relationship between vessels and berths, a reverse semantic knowledge graph of berths is constructed with respect to vessel type, size, and class. This study compares different graph embedding methods, dimensionality reduction techniques, and classification approaches to achieve optimal experimental results. The findings indicate that the vessel type recognition accuracy in the proposed framework reached 83.1%, and the number of Identical Relationships between the recommended and original berths in similar berth recommendations was 3.755. The experiments demonstrate that the framework can provide a technical foundation for deep mining of vessel behavior, vessel type identification, and berth recommendation, as well as a semantic basis for large-scale maritime models.

关键词 ：

Knowledge Graph Ship Classification Intelligent Maritime Graph Embedding Similar Berth Recommendation

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The driver-assisted navigation system is an invaluable tool. However, in intricate scenarios, drivers frequently commit navigation errors. To mitigate this issue, this study focuses on the F-type intersection with the highest incidence of deviations as the research subject. Road scenarios are replicated, and driver behavior data is collected through driving simulator technology. Speed and speed standard deviation are indicators for investigating the influence of driveway distance (DD), navigation prompt timing (NPT), and driver attributes on driving efficiency and safety stability using a generalized linear mixed model (GLMM). Findings reveal that excessively large or small driveway distances and navigation messages that are either premature or delayed negatively affect driving efficiency and safety stability. Consequently, it is recommended to adhere to a driveway distance range of 15-30m, accompanied by the prompt mode of {-300m, -150m, Confirmation}. Furthermore, although no random effects of driver attributes were identified, it is essential to recognize that driver attributes heavily influence their driving behavior in complex road scenarios. This study lays the foundation for optimizing the design of road facilities and navigation systems.

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Cross-view geo-localization of satellite and unmanned aerial vehicles (UAVs) imagery has attracted extensive attention due to its tremendous potential for global navigation satellite system (GNSS) denied navigation. However, inadequate feature representation across different views coupled with positional shifts and distance-scale uncertainty are key challenges. Most of the existing research mainly focused on extracting comprehensive and fine-grained information, yet effective feature representation and alignment should be imposed equal importance. In this article, we propose an innovative transformer-based pipeline TransFG for robust cross-view image matching, which incorporates feature aggregation (FA) and gradient guidance (GG) module. TransFG synergically takes advantage of FA and GG, achieving an effective balance in feature representation and alignment. Specifically, the proposed FA module implicitly learns salient features and dynamically aggregates contextual features from the vision transformer (ViT). The proposed GG module uses the gradient information of local features to further enhance the cross-view feature representation and aligns specific instances across different views. Extensive experiments demonstrate that our pipeline outperforms existing methods in cross-view geo-localization. It achieves an impressive improvement in R@1 and AP than the state-of-the-art (SOTA) methods.

关键词 ：

image matching unmanned aerial vehicles (UAVs) transformer Cross-view geo-localization feature aggregation (FA)

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Traditional oral radiography is prone to problems such as image deviation caused by unstable instruments held by patients and inconsistent projection positions by doctors. In order to achieve high quality and stability of operation, a flexible intelligent medical robot control system has been designed. The radiation tube clamped on the robot can track the center position according to the camera's vision, completing image diagnosis and treatment tasks. To this end, an improved sliding mode tracking control method is proposed, which makes the robot system have good invariance and anti-interference performance. Establish a dynamic model based on the center position of camera vision. A mapping model between the control rate function and joint torque was established by combining the control algorithm with the hyperbolic tangent function. The traditional control algorithm has been optimized to allow the controlled system to move on the predetermined end trajectory of human tissue. Theoretical analysis shows that the control system converges asymptotically. The experimental results validate the effectiveness of the control strategy. It improves the efficiency of intelligent medical operations. Reduce the impact of subjective experiential factors. This in turn reduces the labor intensity of doctors. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词 ：

Intelligent robots Visual servoing Robot vision Quality control Navigation Cameras Radiography Machine design

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With the rapid advancement of smart logistics, the demand for precise and continuous positioning has significantly increased. The integration of the Global Navigation Satellite System (GNSS) and fifth-generation (5G) positioning has attracted substantial attention due to its promising capabilities and cost-effectiveness. To facilitate continuous tracking of logistics items in both indoor and outdoor environments, it is essential to explore the positioning switchover strategy. This paper analyzes the requirements of smart logistics and the prevalent positioning technologies. Then, a GNSS-5G hybrid positioning architecture is considered for the logistics industry. Furthermore, an adaptive positioning switchover strategy based on observable data is proposed, accompanied by a GNSS-5G hybrid positioning case to illustrate the positioning workflow. Finally, the comprehensive analysis of the positioning performance of GNSS and 5G in various scenarios is conducted, which enriches research for future development of positioning technology.

关键词 ：

GNSS logistics 5G switchover strategy hybrid positioning

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Long-span bridges, often exposed to challenging harsh natural environments with severe weather conditions, necessitate real-time examination of load-deformation characteristics to ensure structural integrity and safety. Previous studies have primarily focused on investigating the causes of deformation in bridge structures under different single-load conditions during severe natural disasters, utilizing physics-based, mechanics-based, and data-driven methods. However, these methods cannot achieve fully achieve effective analysis of the real-time effects of multi-factor loads on bridge deformation, particularly in the presence of dynamic and simultaneous loads such as wind or temperature variations. A novel data-driven method is proposed based on a state-of-the-art real-time updating artificial neural networks (ANNs) algorithm to investigate the real-time coupling relationship between multi-loads and bridge deformation, enabling real-time prediction of bridge deformations. Additionally, the real-time characteristics between structural deformation and multi-loads are explained by incorporating SHapley Additive exPlanation (SHAP) in harsh natural environments. The proposed method has been validated on the 1,006-meter Forth Bridge in Scotland, showing high accuracy in real-time displacement prediction. The 9-day testing dataset demonstrated the R-2 values for Y and Z direction deformations were found to be 0.98 and 0.87, respectively. The performance metrics for each day indicated that the majority of Y and Z direction deformations had R-2 values exceeding 0.8, with RMSE and MAE values below 30 mm. The SHAP analysis revealed that an increase in wind speed leads to intensified Y direction deformation (larger SHAP values), while temperature has a significant impact on Z direction deformation (smaller SHAP values). Moreover, the weight influences of each load on the deformation are not fixed. The study's findings demonstrate that the proposed method enables accurate long-term prediction and assessment, allowing precise monitoring and prevention of abnormal risks in bridges under harsh environmental conditions.

关键词 ：

Real-time updating artificial neural networks Structural health monitoring Global navigation satellite systems (GNSS) Harsh natural environments SHapley additive exPlanation Load-deformation characteristics

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Road segmentation is a fundamental task for dynamic map in unmanned aerial vehicle (UAV) path navigation. In unplanned, unknown and even damaged areas, there are usually unpaved roads with blurred edges, deformations and occlusions. These challenges of unpaved road segmentation pose significant challenges to the construction of dynamic maps. Our major contributions have: (1) Inspired by dilated convolution, we propose dilated cross window self-attention (DCWin-Attention), which is composed of a dilated cross window mechanism and a pixel regional module. Our goal is to model the long-range horizontal and vertical road dependencies for unpaved roads with deformation and blurred edges. (2) A shifted cross window mechanism is introduced through coupling with DCWin-Attention to reduce the influence of occluded roads in UAV imagery. In detail, the GVT backbone is constructed by using the DCWin-Attention block for multilevel deep features with global dependency. (3) The unpaved road is segmented with the confidence map generated by fusing the deep features of different levels in a unified perceptual parsing network. We verify our method on the self-established BJUT-URD dataset and public DeepGlobe dataset, which achieves 67.72 and 52.67% of the highest IoU at proper inference efficiencies of 2.7, 2.8 FPS, respectively, demonstrating its effectiveness and superiority in unpaved road segmentation. Our code is available at https://github.com/BJUT-AIVBD/GVT-URS.

关键词 ：

Unpaved road segmentation Dynamic map UAV imagery Global vision transformer DCWin-attention

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With the vigorous development of transportation infrastructure in various countries, the traffic network within the city is becoming more and more complex, and when an emergency occurs in one or more areas of the city, it will inevitably cause traffic congestion in the area and keep spreading. There are still many challenges to solve the urban emergency route planning problem. In this paper, we have employed a double layer search structure, where we have empowered the traditional A* model with a neural network, to construct a region-level dynamic path planning model known as "Double Layer A*". The model divides the road network into two layers, and implements the outer layer and inner layer search. In the outer layer search, we use the historical cab travel data for training to achieve the general direction planning; in the inner layer search, we update the original planning according to the changes of the road condition characteristics of the regional nodes, and perform the re-planning in real time. We conducted experimental evaluations using the road network data of Beijing, and the results showed that compared to a single-layer search structure path planning model, our Double layer A* model planned paths with higher similarity in land characteristics, connectivity, and average connectivity between adjacent nodes, which demonstrates the effectiveness and reasonableness of the Double layer A* model in emergency path planning.

关键词 ：

Search problems neural network Urban areas Routing real-time path planning Roads Map grid Path planning Navigation A* algorithm Planning

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The transportation pipeline of oil and gas buried in the ground will be affected by non-human factors such as geographic natural disasters and some human factors with the accumulation of time, which will lead to settlement and displacement. Using BeiDou satellite navigation system (BDS) positioning technology, radio frequency identification (RFID) technology, combined with data visualization and analysis technology, to analyze the current location status information of underground oil and gas pipelines, and to provide decision-making support for the monitoring of oil and gas pipeline settlement and displacement has become an important research content in the field of oil and gas pipelines. This paper is based on BeiDou high-precision coordinate information dataset and RFID tag information dataset, comprehensive use of geospatial data visualization method and multi-dimensional feature data visualization method, all-round, real-time display of the current pipeline monitoring point location status information, for oil and gas pipeline settlement displacement monitoring and early warning to provide a scientific basis for the improvement of the staff's efficiency of the daily pipeline inspection, timely reporting of the Risks, greatly reducing the frequency of pipeline accidents, effectively protect the safety of oil and gas pipelines.

关键词 ：

radio frequency identification Pipeline monitoring data visualization BeiDou satellite navigation

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The visual quality of 3D-synthesized videos is closely related to the development and broadcasting of immersive media such as free-viewpoint videos and six degrees of freedom navigation. Therefore, studying the 3D-Synthesized video quality assessment is helpful to promote the popularity of immersive media applications. Inspired by the texture compression, depth compression and virtual view synthesis polluting the visual quality of 3D-synthesized videos at pixel-, structure-and content-levels, this paper proposes a Multi-Level 3D-Synthesized Video Quality Assessment algorithm, namely ML-SVQA, which consists of a quality feature perception module and a quality feature regression module. Specifically, the quality feature perception module firstly extracts motion vector fields of the 3D-synthesized video at pixel-, structure-and content-levels by combining the perception mechanism of human visual system. Then, the quality feature perception module measures the temporal flicker distortion intensity in the no-reference environment by calculating the self-similarity of adjacent motion vector fields. Finally, the quality feature regression module uses the machine learning algorithm to learn the mapping of the developed quality features to the quality score. Experiments constructed on the public IRCCyN/IVC and SIAT synthesized video datasets show that our ML-SVQA is more effective than state-of-the-art image/video quality assessment methods in evaluating the quality of 3D-Synthesized videos.

关键词 ：

multi-level 3D-synthesized video Video quality assessment temporal flicker distortion

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