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This document is a special issue of the journal Algorithms, featuring selected papers from the journal's editorial board members from 2022 and 2023. The issue includes 16 research papers covering a range of topics such as game theory, fault detection in cellular networks, optimization algorithms, machine learning, cryptocurrency trading, and more. Each paper presents its own unique research findings and methodologies. The issue aims to showcase the diverse research interests and expertise of the journal's editorial board members. [Extracted from the article]

Millimeter-wave radar-based identification technology has a wide range of applications in persistent identity verification, covering areas such as security production, healthcare, and personalized smart consumption systems. It has received extensive attention from the academic community due to its advantages of being non-invasive, environmentally insensitive and privacy-preserving. Existing identification algorithms mainly rely on a single signal, such as breathing or heartbeat. The reliability and accuracy of these algorithms are limited due to the high similarity of breathing patterns and the low signal-to-noise ratio of heartbeat signals. To address the above issues, this paper proposes an algorithm for multimodal fusion for identity recognition. This algorithm extracts and fuses features derived from phase signals, respiratory signals, and heartbeat signals for identity recognition purposes. The spatial features of signals with different modes are first extracted by the residual network (ResNet), after which these features are fused with a spatial-channel attention fusion module. On this basis, the temporal features are further extracted with a time series-based self-attention mechanism. Finally, the feature vectors of the user's vital sign modality are obtained to perform identity recognition. This method makes full use of the correlation and complementarity between different modal signals to improve the accuracy and reliability of identification. Simulation experiments show that the algorithm identity recognition proposed in this paper achieves an accuracy of 94.26% on a 20-subject self-test dataset, which is much higher than that of the traditional algorithm, which is about 85%. [ABSTRACT FROM AUTHOR]

This paper primarily investigates enhanced object detection techniques for indoor service mobile robots. Robot operating systems (ROS) supply rich sensor data, which boost the models' ability to generalize. However, the model's performance might be hindered by constraints in the processing power, memory capacity, and communication capabilities of robotic devices. To address these issues, this paper proposes an improved you only look once version 5 (YOLOv5)-Lite object detection algorithm based on efficient multi-scale attention and bounding box regression combined with ROS. The algorithm incorporates efficient multi-scale attention (EMA) into the traditional YOLOv5-Lite model and replaces the C3 module with a lightweight C3Ghost module to reduce computation and model size during the convolution process. To enhance bounding box localization accuracy, modified precision-defined intersection over union (MPDIoU) is employed to optimize the model, resulting in the ROS–YOLOv5–FleetEMA model. The results indicated that relative to the conventional YOLOv5-Lite model, the ROS–YOLOv5–FleetEMA model enhanced the mean average precision (mAP) by 2.7% post-training, reduced giga floating-point operations per second (GFLOPS) by 13.2%, and decreased the params by 15.1%. In light of these experimental findings, the model was incorporated into ROS, leading to the development of a ROS-based object detection platform that offers rapid and precise object detection capabilities. [ABSTRACT FROM AUTHOR]

As an indispensable part of the vehicle environment perception task, road traffic marking detection plays a vital role in correctly understanding the current traffic situation. However, the existing traffic marking detection algorithms still have some limitations. Taking lane detection as an example, the current detection methods mainly focus on the location information detection of lane lines, and they only judge the overall attribute of each detected lane line instance, thus lacking more fine-grained dynamic detection of lane line attributes. In order to meet the needs of intelligent vehicles for the dynamic attribute detection of lane lines and more perfect road environment information in urban road environment, this paper constructs a fine-grained attribute detection method for lane lines, which uses pixel-level attribute sequence points to describe the complete attribute distribution of lane lines and then matches the detection results of the lane lines. Realizing the attribute judgment of different segment positions of lane instances is called the fine-grained attribute detection of lane lines (Lane-FGA). In addition, in view of the lack of annotation information in the current open-source lane data set, this paper constructs a lane data set with both lane instance information and fine-grained attribute information by combining manual annotation and intelligent annotation. At the same time, a cyclic iterative attribute inference algorithm is designed to solve the difficult problem of lane attribute labeling in areas without visual cues such as occlusion and damage. In the end, the average accuracy of the proposed algorithm reaches 97% on various types of lane attribute detection. [ABSTRACT FROM AUTHOR]

Efficient maritime search and rescue (SAR) is crucial for responding to maritime emergencies. In traditional SAR, fixed search path planning is inefficient and cannot prioritize high-probability regions, which has significant limitations. To solve the above problems, this paper proposes unmanned surface vehicles (USVs) path planning for maritime SAR based on POS-DQN so that USVs can perform SAR tasks reasonably and efficiently. Firstly, the search region is allocated as a whole using an improved task allocation algorithm so that the task region of each USV has priority and no duplication. Secondly, this paper considers the probability of success (POS) of the search environment and proposes a POS-DQN algorithm based on deep reinforcement learning. This algorithm can adapt to the complex and changing environment of SAR. It designs a probability weight reward function and trains USV agents to obtain the optimal search path. Finally, based on the simulation results, by considering the complete coverage of obstacle avoidance and collision avoidance, the search path using this algorithm can prioritize high-probability regions and improve the efficiency of SAR. [ABSTRACT FROM AUTHOR]

The efficient and accurate identification of traffic signs is crucial to the safety and reliability of active driving assistance and driverless vehicles. However, the accurate detection of traffic signs under extreme cases remains challenging. Aiming at the problems of missing detection and false detection in traffic sign recognition in fog traffic scenes, this paper proposes a recognition algorithm for traffic signs based on pix2pixHD+YOLOv5-T. Firstly, the defogging model is generated by training the pix2pixHD network to meet the advanced visual task. Secondly, in order to better match the defogging algorithm with the target detection algorithm, the algorithm YOLOv5-Transformer is proposed by introducing a transformer module into the backbone of YOLOv5. Finally, the defogging algorithm pix2pixHD is combined with the improved YOLOv5 detection algorithm to complete the recognition of traffic signs in foggy environments. Comparative experiments proved that the traffic sign recognition algorithm proposed in this paper can effectively reduce the impact of a foggy environment on traffic sign recognition. Compared with the YOLOv5-T and YOLOv5 algorithms in moderate fog environments, the overall improvement of this algorithm is achieved. The precision of traffic sign recognition of the algorithm in the fog traffic scene reached 78.5%, the recall rate was 72.2%, and mAP@0.5 was 82.8%. [ABSTRACT FROM AUTHOR]

Compared to the conventional boost power factor correction (PFC) converter, a totem-pole bridgeless PFC has high efficiency because it does not have an input diode rectifier stage, but a current spike may occur when the polarity of the grid voltage changes. This paper proposes a digital control algorithm for bridgeless totem-pole PFC with a simple control structure based on the phase angle of grid voltage. The proposed algorithm has a PI-based double-loop control structure and performs DC-link voltage and input inductor current control. Rectifying switches operate based on the proposed rectification algorithm using phase angle information calculated through a single-phase phase-locked loop (PLL) to prevent current spikes. The feed-forward duty ratio value is calculated according to the polarity of the grid voltage and added to the double-loop controller to perform appropriate power factor control. The performance and feasibility of the proposed control algorithm are verified through a 3 kW hardware prototype. [ABSTRACT FROM AUTHOR]

Simultaneous Localization and Mapping (SLAM) enables mobile robots to autonomously perform localization and mapping tasks in unknown environments. Despite significant progress achieved by visual SLAM systems in ideal conditions, relying solely on a single robot and point features for mapping in large-scale indoor environments with weak-texture structures can affect mapping efficiency and accuracy. Therefore, this paper proposes a multi-robot collaborative mapping method based on point-line fusion to address this issue. This method is designed for indoor environments with weak-texture structures for localization and mapping. The feature-extraction algorithm, which combines point and line features, supplements the existing environment point feature-extraction method by introducing a line feature-extraction step. This integration ensures the accuracy of visual odometry estimation in scenes with pronounced weak-texture structure features. For relatively large indoor scenes, a scene-recognition-based map-fusion method is proposed in this paper to enhance mapping efficiency. This method relies on visual bag of words to determine overlapping areas in the scene, while also proposing a keyframe-extraction method based on photogrammetry to improve the algorithm's robustness. By combining the Perspective-3-Point (P3P) algorithm and Bundle Adjustment (BA) algorithm, the relative pose-transformation relationships of multi-robots in overlapping scenes are resolved, and map fusion is performed based on these relative pose relationships. We evaluated our algorithm on public datasets and a mobile robot platform. The experimental results demonstrate that the proposed algorithm exhibits higher robustness and mapping accuracy. It shows significant effectiveness in handling mapping in scenarios with weak texture and structure, as well as in small-scale map fusion. [ABSTRACT FROM AUTHOR]

This paper proposes a deep learning-based intelligent detection device for insulation pull rod defects, addressing the issues of low detection accuracy, poor timeliness of intelligent analysis, and the difficulty in preserving detection results. Firstly, by constructing the pull rod defects dataset and training the YOLOv5s network, along with commonly used object detection algorithms in industrial defect detection, the feasibility of deep learning networks for insulation pull rod defects detection is explored. Secondly, the trained model is combined to build an intelligent detection device for pull rod defects, integrating insulation pull rod image acquisition and defect detection into a unified system. The research results demonstrate that the YOLOv5s network can quickly and accurately detect pull rod defects. On the test set constructed in this paper, the detection performance metric mAP@0.5:0.95 of the trained model reached 54.7%. Specifically, the mAP@0.5 score was 86.9% at a threshold of 0.5. The detection speed FPS reached 169.5, significantly improving the detection efficiency and accuracy compared to traditional object detection algorithms. By establishing an organic connection between the image hardware acquisition device and the deep learning network, the existing problems of inefficient detection and difficult storage of detection results in pull rod defects detection methods are effectively addressed. This research provides new insights for detecting insulation pull rod defects. [ABSTRACT FROM AUTHOR]

Most image quality assessment (IQA) algorithms based on sparse representation primarily focus on amplitude information, often overlooking the structural composition of images. However, structural composition is closely linked to perceived image quality, a connection that existing methods do not adequately address. To fill this gap, this paper proposes a novel distorted-image quality assessment algorithm based on a sparse structure and subjective perception (IQA-SSSP). This algorithm evaluates the quality of distorted images by measuring the sparse structure similarity between a reference and distorted images. The proposed method has several advantages. First, the sparse structure algorithm operates with reduced computational complexity, leading to faster processing speeds, which makes it suitable for practical applications. Additionally, it efficiently handles large-scale data, further enhancing the assessment process. Experimental results validate the effectiveness of the algorithm, showing that it achieves a high correlation with human visual perception, as reflected in both objective and subjective evaluations. Specifically, the algorithm yielded a Pearson correlation coefficient of 0.929 and a mean squared error of 8.003, demonstrating its robustness and efficiency. By addressing the limitations of existing IQA methods and introducing a more holistic approach, this paper offers new perspectives on IQA. The proposed algorithm not only provides reliable quality assessment results but also closely aligns with human visual experience, thereby enhancing both the objectivity and accuracy of image quality evaluations. This research offers significant theoretical support for the advancement of sparse representation in IQA. [ABSTRACT FROM AUTHOR]

This paper presents a fast focusing inversion algorithm of magnetic data based on the conjugate gradient method, which can be used to describe the underground target geologic body efficiently and clearly. The proposed method realizes an effect similar to matrix compression by changing the computation order, calculating the inner product of vectors and equivalent expansion of expressions. Model tests show that this strategy successfully reduces the computation time of a single iteration of the conjugate gradient method, so the three-dimensional magnetic data inversion is realized under a certain number of iterations. In this paper, the detailed calculation steps of the proposed inversion method are given, and the effectiveness and high efficiency of the proposed fast focusing inversion method are verified by three theoretical model tests and a set of measured data. Finally, the fast focus inversion algorithm is applied to the magnetic data of Gonghe Basin, Qinghai Province, to describe the spatial distribution range of deep hot dry rock, which provides a direction for the continuous exploration of geothermal resources in this area. [ABSTRACT FROM AUTHOR]

Process discovery aims to discover process models from event logs to describe actual business processes. The quality of event logs has an impact on the quality of process models, so preprocessing methods can be used to improve the quality of event logs. Chaotic activities may exist in real business scenarios, and the occurrence of chaotic activities is independent of other activities in the process and can occur at any location in the event log at any frequency. Therefore, chaotic activities seriously affect the model quality of process discovery. Filtering chaotic activities in event logs can effectively improve the quality of event logs and thus improve the quality of process models. The traditional chaotic activity filtering algorithm makes it difficult to balance accuracy and time performance. Therefore, a direct method for filtering chaotic activities is proposed in this paper. By analyzing the relationship between activities, chaotic activities are identified in the log according to the characteristics of chaotic activities and the direct following relationship of activities as the judgment condition, and the filtering of chaotic activities in the event log is realized. In addition, this paper proposes an indirect chaotic activity filtering method, which identifies and filters chaotic activities in the log by analyzing the influence of the existence of different activities on the overall chaos degree of the log. The proposed method is compared with the traditional chaotic activity filtering method on several simulation/real data sets, and the accuracy and running time between the multi-group event logs and the process models generated before and after chaotic activity filtering are analyzed, further verifying the effectiveness and feasibility of the proposed method. By summarizing the experimental results, it is found that the accuracy of the proposed chaotic activity filtering methods is greater than that of the frequency-based filtering method and is close to that of the entropy-based chaotic activity filtering methods. Moreover, compared with other filtering methods used in the experiment, the chaotic activity filtering method proposed in this paper can improve the efficiency by 23.4% on average for simulation logs, and by 84.25% on average for real event logs. It is concluded that compared with other filtering methods, the proposed chaotic activity filtering methods have higher accuracy and can effectively improve the time performance of chaotic activity filtering. Therefore, the chaotic activity filtering method proposed in this paper can balance the accuracy and time performance, and can ensure the integrity of the filtered event log to a certain extent. [ABSTRACT FROM AUTHOR]

This paper introduces a UWB-based human-following system for polar-exploration robots, integrating obstacle and crevasse avoidance functions to enhance the safety and efficiency of explorers in extreme environments. The proposed system determines the relative position of the explorer using UWB anchors and tags. It also utilizes real-time local obstacle mapping and path-planning algorithms to find safe paths that avoid collisions with obstacles. Simulation and real-world experiments confirm that the proposed system operates effectively in polar environments, reducing the operational burden on explorers and increasing mission success rates. [ABSTRACT FROM AUTHOR]

In order to improve the reading efficiency of pointer meter, this paper proposes a reading method based on LinkNet. Firstly, the meter dial area is detected using YOLOv8. Subsequently, the detected images are fed into the improved LinkNet segmentation network. In this network, we replace traditional convolution with partial convolution, which reduces the number of model parameters while ensuring accuracy is not affected. Remove one pair of encoding and decoding modules to further compress the model size. In the feature fusion part of the model, the CBAM (Convolutional Block Attention Module) attention module is added and the direct summing operation is replaced by the AFF (Attention Feature Fusion) module, which enhances the feature extraction capability of the model for the segmented target. In the subsequent rotation correction section, this paper effectively addresses the issue of inaccurate prediction by CNN networks for axisymmetric images within the 0–360° range, by dividing the rotation angle prediction into classification and regression steps. It ensures that the final reading part receives the correct angle of image input, thereby improving the accuracy of the overall reading algorithm. The final experimental results indicate that our proposed reading method has a mean absolute error of 0.20 and a frame rate of 15. [ABSTRACT FROM AUTHOR]

Effective path planning is crucial for autonomous mobile robots navigating complex environments. The "global–local" coupled path planning algorithm exhibits superior global planning capabilities and local adaptability. However, these algorithms often fail to fully realize their potential due to low efficiency and excessive constraints. To address these issues, this study introduces a simpler and more effective integration strategy. Specifically, this paper proposes using a bi-layer map and a feasible domain strategy to organically combine the D*Lite algorithm with the Dynamic Window Approach (DWA). The bi-layer map effectively reduces the number of nodes in global planning, enhancing the efficiency of the D*Lite algorithm. The feasible domain strategy decreases constraints, allowing the local algorithm DWA to utilize its local planning capabilities fully. Moreover, the cost functions of both the D*Lite algorithm and DWA have been refined, enabling the fused algorithm to cope with more complex environments. This paper conducts simulation experiments across various settings and compares our method with A_DWA, another "global–local" coupled approach, which combines A* and DWA. D_DWA significantly outperforms A_DWA in complex environments, despite a 7.43% increase in path length. It reduces the traversal of risk areas by 71.95%, accumulative risk by 80.34%, global planning time by 26.98%, and time cost by 35.61%. Additionally, D_DWA outperforms the A_Q algorithm, a coupled approach validated in real-world environments, which combines A* and Q-learning, achieving reductions of 1.34% in path length, 67.14% in traversal risk area, 78.70% in cumulative risk, 34.85% in global planning time, and 37.63% in total time cost. The results demonstrate the superiority of our proposed algorithm in complex scenarios. [ABSTRACT FROM AUTHOR]

Clouds are crucial in regulating the Earth's energy budget. Global cloud top heights have been easily retrieved from satellite measurements, but there are few methods for determining cloud base height (CBH) from satellite measurements. The Cloud Base Altitude Spatial Extrapolator (CBASE) algorithm was proposed to derive the height of the lower-troposphere liquid cloud base by using the Cloud-Aerosol Lidar with Orthogonal polarization cloud aerosol LiDAR (CALIOP) profiles and weather observations at airports from aviation routine and special weather report (METARs and SPECIs, called METAR) observation data in the United States. A modification to the CBASE algorithm over China (CNMETAR-CBASE) is presented in this paper. In this paper, the ability of the CBASE algorithm to calculate CBH in China is evaluated, and METAR observations over China (CNMETAR) were then used to modify the CBASE algorithm. The results including CNMETAR observation data in China can better retrieve CBH over China compared with the results using the original CBASE algorithm, and the accuracy of the global CBH results has been improved. Overestimations of CBH with the original algorithm range from 500 to 800 m in China, which have been reduced to about 300 m with an improved algorithm. The deviations calculated by the algorithm also have a significant reduction, from 480 m (CBASE) to 420 m (CNMETAR-CBASE). In conclusion, the modified CBASE algorithm not only calculates the CBH more accurately in China but also improves the results of the global CBH retrieved from satellites. [ABSTRACT FROM AUTHOR]

The adoption of blockchain platforms to bolster the security of Internet of Things (IoT) systems has attracted significant attention in recent years. Currently, there is a lack of comprehensive and systematic survey papers in the literature addressing these platforms. This paper discusses six of the most popular emerging blockchain platforms adopted by IoT systems and analyses their usage in state-of-the-art works to solve security problems. The platform was compared in terms of security features and other requirements. Findings from the study reveal that most blockchain components contribute directly or indirectly to IoT security. Blockchain platform components such as cryptography, consensus mechanism, and hashing are common ways that security is achieved in all blockchain platform for IoT. Technologies like Interplanetary File System (IPFS) and Transport Layer Security (TLS) can further enhance data and communication security when used alongside blockchain. To enhance the applicability of blockchain in resource-constrained IoT environments, future research should focus on refining cryptographic algorithms and consensus mechanisms to optimise performance and security. [ABSTRACT FROM AUTHOR]

The ability to make informed decisions in complex scenarios is crucial for intelligent automotive systems. Traditional expert rules and other methods often fall short in complex contexts. Recently, reinforcement learning has garnered significant attention due to its superior decision-making capabilities. However, there exists the phenomenon of inaccurate target network estimation, which limits its decision-making ability in complex scenarios. This paper mainly focuses on the study of the underestimation phenomenon, and proposes an end-to-end autonomous driving decision-making method based on an improved TD3 algorithm. This method employs a forward camera to capture data. By introducing a new critic network to form a triple-critic structure and combining it with the target maximization operation, the underestimation problem in the TD3 algorithm is solved. Subsequently, the multi-timestep averaging method is used to address the policy instability caused by the new single critic. In addition, this paper uses Carla platform to construct multi-vehicle unprotected left turn and congested lane-center driving scenarios and verifies the algorithm. The results demonstrate that our method surpasses baseline DDPG and TD3 algorithms in aspects such as convergence speed, estimation accuracy, and policy stability. [ABSTRACT FROM AUTHOR]

This paper improves the timeliness of satellite mission planning to cope with the rapid response to changes. In this paper, satellite mission planning is investigated. Firstly, the satellite dynamics model and mission planning model are established, and an improved Monte Carlo tree (Improved-MCTS) algorithm is proposed, which utilizes the Monte Carlo tree search in combination with the state uncertainty network (State-UN) to reduce the time of exploring the nodes (At the MCTS selection stage, the exploration of nodes specifically refers to the algorithm needing to decide whether to choose nodes that have already been visited (exploitation) or nodes that have not been visited yet (exploration)). The results show that this algorithm performs better in terms of profit (in this paper, the observation task is given a weight of 0–1, and each planned task will receive a profit; that is, a profit will be assigned at the initial moment) and convergence speed compared to the ant colony algorithm (ACO) and the asynchronous advantage actor critic (A3C). [ABSTRACT FROM AUTHOR]

In autonomous driving, the fusion of multiple sensors is considered essential to improve the accuracy and safety of 3D object detection. Currently, a fusion scheme combining low-cost cameras with highly robust radars can counteract the performance degradation caused by harsh environments. In this paper, we propose the IRBEVF-Q model, which mainly consists of BEV (Bird's Eye View) fusion coding module and an object decoder module.The BEV fusion coding module solves the problem of unified representation of different modal information by fusing the image and radar features through 3D spatial reference points as a medium. The query in the object decoder, as a core component, plays an important role in detection. In this paper, Heat Map-Guided Query Initialization (HGQI) and Dynamic Position Encoding (DPE) are proposed in query construction to increase the a priori information of the query. The Auxiliary Noise Query (ANQ) then helps to stabilize the matching. The experimental results demonstrate that the proposed fusion model IRBEVF-Q achieves an NDS of 0.575 and a mAP of 0.476 on the nuScenes test set. Compared to recent state-of-the-art methods, our model shows significant advantages, thus indicating that our approach contributes to improving detection accuracy. [ABSTRACT FROM AUTHOR]

Publicly available ionograms are often in the form of pictures. This paper proposes a novel algorithm for extracting and repairing the F layer traces from pictorial ionograms. Extensive efforts have been invested in ionogram autoscaling and critical parameter identification to improve the efficiency of scaling algorithms. To obtain the parameters of the F layer automatically, it is necessary to accurately extract the F layer trace. However, research on F layer trace extraction with repair is relatively limited. The method employed in this study makes full use of the characteristics of different types of echoes on the ionograms, and the procedure includes noise preprocessing, coupling noise processing, and trace repair. To enhance the applicability of the repair, two different automatic filling algorithms are adopted to repair the F layer trace. The aim of this paper is to present an adaptive algorithm to automatically extract and repair F layer traces from different pictorial ionograms. The results of Hainan Fuke ionograms illustrate the reliability of the F layer trace extraction and trace repair. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the traditional rapidly exploring random tree (RRT) algorithm only considers the global path of unmanned vehicles in a static environment, which has the limitation of not being able to avoid unknown dynamic obstacles in real time, and that the traditional dynamic window approach (DWA) algorithm is prone to fall into a local optimum during local path planning, this paper proposes a path planning method for unmanned vehicles that integrates improved RRT and DWA algorithms. The RRT algorithm is improved by introducing strategies such as target-biased random sampling, adaptive step size, and adaptive radius node screening, which enhance the efficiency and safety of path planning. The global path key points generated by the improved RRT algorithm are used as the subtarget points of the DWA algorithm, and the DWA algorithm is optimized through the design of an adaptive evaluation function weighting method based on real-time obstacle distances to achieve more reasonable local path planning. Through simulation experiments, the fusion algorithm shows promising results in a variety of typical static and dynamic mixed driving scenarios, can effectively plan a path that meets the driving requirements of an unmanned vehicle, avoids unknown dynamic obstacles, and shows higher path optimization efficiency and driving stability in complex environments, which provides strong support for an unmanned vehicle's path planning in complex environments. [ABSTRACT FROM AUTHOR]

This paper presents a valley path planning algorithm based on the Hybrid A* algorithm. This algorithm is aimed at finding the valley path for aircraft considering dynamics constraints and terrain limitations. The preliminaries involve the establishment of a 3D workspace based on digital elevation map (DEM) data and addressing methods of valley detection. Following this comprehensive groundwork, the Hybrid A*-based algorithm, employed to determine the valley path within the 3D workspace while accommodating dynamic constraints and terrain limitations, is then introduced. In the experimental test, to validate the effectiveness of the algorithm proposed in this paper, we tested the performance of the proposed algorithm and other three baseline algorithms based on four optimization objectives in three workspaces. The simulated results indicate that the algorithm proposed in this paper can effectively find the valley path while considering dynamic constraints and terrain limitations. [ABSTRACT FROM AUTHOR]

The integration of machine learning (ML) techniques into marketing strategies has become increasingly relevant in modern business. Utilizing scientific manuscripts indexed in the Scopus database, this article explores how this integration is being carried out. Initially, a focused search is undertaken for academic articles containing both the terms "machine learning" and "marketing" in their titles, which yields a pool of papers. These papers have been processed using the Supabase platform. The process has included steps like text refinement and feature extraction. In addition, our study uses two key ML methodologies: topic modeling through NMF and a comparative analysis utilizing the k-means clustering algorithm. Through this analysis, three distinct clusters emerged, thus clarifying how ML techniques are influencing marketing strategies, from enhancing customer segmentation practices to optimizing the effectiveness of advertising campaigns. [ABSTRACT FROM AUTHOR]

Oblique photography is a regional digital surface model generation technique that can be widely used for building 3D model construction. However, due to the lack of geometric and semantic information about the building, these models make it difficult to differentiate more detailed components in the building, such as roofs and balconies. This paper proposes a deep learning-based method (U-NET) for constructing 3D models of low-rise buildings that address the issues. The method ensures complete geometric and semantic information and conforms to the LOD2 level. First, digital orthophotos are used to perform building extraction based on U-NET, and then a contour optimization method based on the main direction of the building and the center of gravity of the contour is used to obtain the regular building contour. Second, the pure building point cloud model representing a single building is extracted from the whole point cloud scene based on the acquired building contour. Finally, the multi-decision RANSAC algorithm is used to segment the building detail point cloud and construct a triangular mesh of building components, followed by a triangular mesh fusion and splicing method to achieve monolithic building components. The paper presents experimental evidence that the building contour extraction algorithm can achieve a 90.3% success rate and that the resulting single building 3D model contains LOD2 building components, which contain detailed geometric and semantic information. [ABSTRACT FROM AUTHOR]

In the field of risk assessment, the traditional econometric models are generally used to assess credit risk. And with the introduction of the "dual-carbon" goals to promote the development of a low-carbon economy, the scale of green credit in China has rapidly expanded. But with the advent of the big data era, due to the poor interpretability of a traditional single machine learning model, it is difficult to capture nonlinear relationships, and there are shortcomings in prediction accuracy and robustness. This paper selects the adjusted ensemble learning model based on the homogeneous and heterogeneous factors for user default prediction, which can efficiently process large quantities of high-dimensional data. This article adjusts each model to adapt to the task and innovatively compares various models. In this paper, the missing value filling method, feature selection, and ensemble model are studied and discussed, and the optimal ensemble model is obtained. When comparing the predictions of single models and ensemble models, the accuracy, sensitivity, specificity, F1-Score, Kappa, and MCC of Categorical Features Gradient Boosting (CatBoost) and Random undersampling Boosting (RUSBoost) all reach 100%. The experimental results prove that the algorithm based on adjusted homogeneous and heterogeneous ensemble learning can predict the user default efficiently and accurately. This paper also provides some references for establishing a risk assessment index system. [ABSTRACT FROM AUTHOR]

Efficient multiprocessor scheduling is pivotal in optimizing the performance of parallel computing systems. This paper leverages the power of Petri nets and the tool GPenSIM to model and simulate a variety of multiprocessor scheduling algorithms (the basic algorithms such as first come first serve, shortest job first, and round robin, and more sophisticated schedulers like multi-level feedback queue and Linux's completely fair scheduler). This paper presents the evaluation of three crucial performance metrics in multiprocessor scheduling (such as turnaround time, response time, and throughput) under various scheduling algorithms. However, the primary focus of the paper is to develop a robust simulation platform consisting of Petri Modules to facilitate the dynamic representation of concurrent processes, enabling us to explore the real-time interactions and dependencies in a multiprocessor environment; more advanced and newer schedulers can be tested with the simulation platform presented in this paper. [ABSTRACT FROM AUTHOR]

The research on additive manufacturing (AM) path planning mainly focuses on the traditional three-axis AM path planning and five-degree-of-freedom (DOF) AM path planning, while there is less research on six-DOF AM path planning. In the traditional AM path planning algorithm, the filling path is discontinuous and there is long straight-line printing in a certain direction, which can easily lead to warpage deformation. Therefore, in this work, the six-DOF manipulator is taken as the main object to build an AM platform, and the mechanism of AM path planning of the manipulator is studied. The path planning algorithm combining the contour offset filling method and Hilbert curve filling is optimized by using a cubic uniform B-spline curve, and an AM path planning algorithm suitable for a six-DOF manipulator is obtained. A continuous printing path can be generated by this algorithm. It reduces the existence of long straight-line printing in a certain direction, thereby reducing the warpage deformation of the model and improving the molding quality of the model. The traditional three-axis AM device and the six-DOF AM platform were used to print two kinds of models. By comparing the printing time, the six-DOF AM platform was 43.70% and 37.94% shorter than the traditional three-axis AM device. The same model was printed on a six-DOF AM platform by using the parallel scanning filling method, the path planning algorithm combining contour offset and Hilbert curve, and the method proposed in this paper. Through experimental verification, the average warpage deformation of the model printed by the method proposed in this paper was reduced by 37.81% and 13.79%, respectively, compared with the other two methods. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV)-based object detection methods are widely used in traffic detection due to their high flexibility and extensive coverage. In recent years, with the increasing complexity of the urban road environment, UAV object detection algorithms based on deep learning have gradually become a research hotspot. However, how to further improve algorithmic efficiency in response to the numerous and rapidly changing road elements, and thus achieve high-speed and accurate road object detection, remains a challenging issue. Given this context, this paper proposes the high-efficiency multi-object detection algorithm for UAVs (HeMoDU). HeMoDU reconstructs a state-of-the-art, deep-learning-based object detection model and optimizes several aspects to improve computational efficiency and detection accuracy. To validate the performance of HeMoDU in urban road environments, this paper uses the public urban road datasets VisDrone2019 and UA-DETRAC for evaluation. The experimental results show that the HeMoDU model effectively improves the speed and accuracy of UAV object detection. [ABSTRACT FROM AUTHOR]

This paper focuses on the problem of information source identification in online social networks (OSNs). By analyzing the research situation of source identification problems and challenges (such as the randomness of the information dissemination process and complexity of the underlying network topology), this paper studies the problem of multiple source diffusion and proposes a source identification algorithm based on multi-attribute topological clustering (MaTC). The basic idea of the algorithm is to decompose the multi-source problems into a series of single-source problems by using clustering partitioning to improve accuracy and efficiency. Firstly, it estimates the number of source nodes, which is also the number of network partitions, then characterizes the combination of multiple attribute structures as an attribute index of topological clustering, performs an analysis of the distribution of real source nodes in each partition to evaluate the accuracy of the clustering partition, and finally uses Jordan centrality within each partition for single-source identification. Through comparative experiments, it is verified that the proposed MaTC algorithm is superior to the comparison algorithms in evaluating indicators. [ABSTRACT FROM AUTHOR]

In response to the challenges posed by small objects in remote sensing images, such as low resolution, complex backgrounds, and severe occlusions, this paper proposes a lightweight improved model based on YOLOv8n. During the detection of small objects, the feature fusion part of the YOLOv8n algorithm retrieves relatively fewer features of small objects from the backbone network compared to large objects, resulting in low detection accuracy for small objects. To address this issue, firstly, this paper adds a dedicated small object detection layer in the feature fusion network to better integrate the features of small objects into the feature fusion part of the model. Secondly, the SSFF module is introduced to facilitate multi-scale feature fusion, enabling the model to capture more gradient paths and further improve accuracy while reducing model parameters. Finally, the HPANet structure is proposed, replacing the Path Aggregation Network with HPANet. Compared to the original YOLOv8n algorithm, the recognition accuracy of mAP@0.5 on the VisDrone data set and the AI-TOD data set has increased by 14.3% and 17.9%, respectively, while the recognition accuracy of mAP@0.5:0.95 has increased by 17.1% and 19.8%, respectively. The proposed method reduces the parameter count by 33% and the model size by 31.7% compared to the original model. Experimental results demonstrate that the proposed method can quickly and accurately identify small objects in complex backgrounds. [ABSTRACT FROM AUTHOR]

This paper focuses on predicting road passenger probability and optimizing taxi driving routes based on trajectory big data. By utilizing clustering algorithms to identify key passenger points, a method for calculating and predicting road passenger probability is proposed. This method calculates the passenger probability for each road segment during different time periods and uses a BiLSTM neural network for prediction. A passenger-seeking recommendation model is then constructed with the goal of maximizing passenger probability, and it is solved using the NSGA-II algorithm. Experiments are conducted on the Chengdu taxi trajectory dataset, using MSE as the metric for model prediction accuracy. The results show that the BiLSTM prediction model improves prediction accuracy by 9.67% compared to the BP neural network and by 6.45% compared to the LSTM neural network. The proposed taxi driver passenger-seeking route selection method increases the average passenger probability by 18.95% compared to common methods. The proposed passenger-seeking recommendation framework, which includes passenger probability prediction and route optimization, maximizes road passenger efficiency and holds significant academic and practical value. [ABSTRACT FROM AUTHOR]

In response to the challenges of small-size defects and low recognition rates in Printed Circuit Boards (PCBs), as well as the need for lightweight detection models that can be embedded in portable devices, this paper proposes an improved defect detection method based on a lightweight shared convolutional head using YOLOv8n. Firstly, the Spatial and Channel reconstruction Convolution (SCConv) is embedded into the Cross Stage Partial with Convolutional Layer Fusion (C2f) structure of the backbone network, which reduces redundant computations and enhances the model's learning capacity. Secondly, an adaptive feature selection module is integrated to improve the network's ability to recognize small targets. Subsequently, a Shared Lightweight Convolutional Detection (SLCD) Head replaces the original Decoupled Head, reducing the model's computational complexity while increasing detection accuracy. Finally, the Weighted Intersection over Union (WIoU) loss function is introduced to provide more precise evaluation results and improve generalization capability. Comparative experiments conducted on a public PCB dataset demonstrate that the improved algorithm achieves a mean Average Precision (mAP) of 98.6% and an accuracy of 99.8%, representing improvements of 3.8% and 3.1%, respectively, over the original model. The model size is 4.1 M, and its FPS is 144.1, meeting the requirements for real-time and lightweight portable deployment. [ABSTRACT FROM AUTHOR]

In abstract argumentation frameworks, the computation of stable extensions is an important semantic task for evaluating the acceptability of arguments. The current approaches for the computation of stable extensions are typically conducted through methodologies that are either label-based or extension-based. Label-based algorithms operate by assigning labels to each argument, thus reducing the attack relations between arguments to constraint relations among the labels. This paper analyzes the existing two-label and three-label enumeration algorithms for stable extensions through case studies. It is found that both the two-label and three-label algorithms are not precise enough in defining types of arguments. To address these issues, this paper proposes a four-label enumeration algorithm for stable extensions. This method introduces a m u s t _ i n label to pre-mark certain i n -type arguments, thereby achieving a finer classification of i n -type arguments. This enhances the labelings' propagation ability and reduces the algorithm's search space. Our proposed four-label algorithm was tested on authoritative benchmark sets of abstract argumentation framework problems: ICCMA 2019, ICCMA 2021, and ICCMA 2023. Experimental results show that the four-label algorithm significantly improves solving efficiency compared to existing two-label and three-label algorithms. Additionally, ablation experiments confirm that both the four-label transition strategy and preprocessing strategy enhance the algorithm's performance. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of trajectory planning for autonomous vehicles operating in complex, constrained environments. The proposed method enhances the hybrid A-star algorithm through back-end optimization. An adaptive node expansion strategy is introduced to handle varying environmental complexities. By integrating Dijkstra's shortest path search, the method improves direction selection and refines the estimated cost function. Utilizing the characteristics of hybrid A-star path planning, a quadratic programming approach with designed constraints smooths discrete path points. This results in a smoothed trajectory that supports speed planning using S-curve profiles. Both simulation and experimental results demonstrate that the improved hybrid A-star search significantly boosts efficiency. The trajectory shows continuous and smooth transitions in heading angle and speed, leading to notable improvements in trajectory planning efficiency and overall comfort for autonomous vehicles in challenging environments. [ABSTRACT FROM AUTHOR]

In the field of industrial inspection, accurate detection of thread quality is crucial for ensuring mechanical performance. Existing machine-vision-based methods for internal thread defect detection often face challenges in efficient detection and sufficient model training samples due to the influence of mechanical geometric features. This paper introduces a novel image acquisition structure, proposes a data augmentation algorithm based on Generative Adversarial Networks (GANs) to effectively construct high-quality training sets, and employs a YOLO algorithm to achieve internal thread defect detection. Through multi-metric evaluation and comparison with external threads, high-similarity internal thread image generation is achieved. The detection accuracy for internal and external threads reached 94.27% and 93.92%, respectively, effectively detecting internal thread defects. [ABSTRACT FROM AUTHOR]

In this paper, a consensus-based model predictive control (Cb-MPC) scheme is proposed to control the active power and voltage at all nodes in grid-connected active distribution networks (ADNs) with multiple distributed energy resources (DERs). The proposed design methodology is based on a multiple-input multiple-output (MIMO) model of an ADN which accounts for both the internal and external interactions among the control loops of the DERs. To achieve the control objective, each DER unit is equipped with a controller–observer system. In particular, the observer implements the consensus algorithm to estimate the collective system state by exchanging data only with its neighbors. The scope of the controller is to solve the MPC optimal problem based on its collective state estimate, and, due to the presence of an integral term in the control action, it is robust against any unknown scenarios of the ADN, which are represented by uncertainty in the model parameters. The results of numerical simulations validate the effectiveness of the proposed method in the presence of unknown changes in the operating conditions of the ADN and of communication using a sample and hold function. [ABSTRACT FROM AUTHOR]

This paper employs an integral reinforcement learning (IRL) method to investigate the optimal tracking control problem (OTCP) for nonlinear nonzero-sum (NZS) differential game systems with unknown drift dynamics. Unlike existing methods, which can only bound the tracking error, the proposed approach ensures that the tracking error asymptotically converges to zero. This study begins by constructing an augmented system using the tracking error and reference signal, transforming the original OTCP into solving the coupled Hamilton–Jacobi (HJ) equation of the augmented system. Because the HJ equation contains unknown drift dynamics and cannot be directly solved, the IRL method is utilized to convert the HJ equation into an equivalent equation without unknown drift dynamics. To solve this equation, a critic neural network (NN) is employed to approximate the complex value function based on the tracking error and reference information data. For the unknown NN weights, the least squares (LS) method is used to design an estimation law, and the convergence of the weight estimation error is subsequently proven. The approximate solution of optimal control converges to the Nash equilibrium, and the tracking error asymptotically converges to zero in the closed system. Finally, we validate the effectiveness of the proposed method in this paper based on MATLAB using the ode45 method and least squares method to execute Algorithm 2. [ABSTRACT FROM AUTHOR]

In Mobile Crowdsensing (MCS), sensing tasks have different impacts and contributions to the whole system or specific targets, so the importance of the tasks is different. Since resources for performing tasks are usually limited, prioritizing the allocation of resources to more important tasks can ensure that key data or information can be collected promptly and accurately, thus improving overall efficiency and performance. Therefore, it is very important to consider the importance of tasks in the task selection and allocation of MCS. In this paper, a task queue is established, the importance of tasks, the ability of participants to perform tasks, and the stability of the task queue are considered, and a novel task selection and allocation scheme (TSAS) in the MCS system is designed. This scheme introduces the Lyapunov optimization method, which can be used to dynamically keep the task queue stable, balance the execution ability of participants and the system load, and perform more important tasks in different system states, even when the participants are limited. In addition, the Double Deep Q-Network (DDQN) method is introduced to improve on the traditional solution of the Lyapunov optimization problem, so this scheme has a certain predictive ability and foresight on the impact of future system states. This paper also proposes action-masking and iterative training methods for the MCS system, which can accelerate the training process of the neural network in the DDQN and improve the training effect. Experiments show that the TSAS based on the Lyapunov optimization method and DDQN performs better than other algorithms, considering the long-term stability of the queue, the number and importance of tasks to be executed, and the congestion degree of tasks. [ABSTRACT FROM AUTHOR]

Typical satellite component detection is an application-valuable and challenging research field. Currently, there are many algorithms for detecting typical satellite components, but due to the limited storage space and computational resources in the space environment, these algorithms generally have the problem of excessive parameter count and computational load, which hinders their effective application in space environments. Furthermore, the scale of datasets used by these algorithms is not large enough to train the algorithm models well. To address the above issues, this paper first applies YOLOv8 to the detection of typical satellite components and proposes a Lightweight Typical Satellite Components Detection algorithm based on improved YOLOv8 (LTSCD-YOLO). Firstly, it adopts the lightweight network EfficientNet-B0 as the backbone network to reduce the model's parameter count and computational load; secondly, it uses a Cross-Scale Feature-Fusion Module (CCFM) at the Neck to enhance the model's adaptability to scale changes; then, it integrates Partial Convolution (PConv) into the C2f (Faster Implementation of CSP Bottleneck with two convolutions) module and Re-parameterized Convolution (RepConv) into the detection head to further achieve model lightweighting; finally, the Focal-Efficient Intersection over Union (Focal-EIoU) is used as the loss function to enhance the model's detection accuracy and detection speed. Additionally, a larger-scale Typical Satellite Components Dataset (TSC-Dataset) is also constructed. Our experimental results show that LTSCD-YOLO can maintain high detection accuracy with minimal parameter count and computational load. Compared to YOLOv8s, LTSCD-YOLO improved the mean average precision (mAP50) by 1.50% on the TSC-Dataset, reaching 94.5%. Meanwhile, the model's parameter count decreased by 78.46%, the computational load decreased by 65.97%, and the detection speed increased by 17.66%. This algorithm achieves a balance between accuracy and light weight, and its generalization ability has been validated on real images, making it effectively applicable to detection tasks of typical satellite components in space environments. [ABSTRACT FROM AUTHOR]

Object detection via remote sensing encounters significant challenges due to factors such as small target sizes, uneven target distribution, and complex backgrounds. This paper introduces the K-CBST YOLO algorithm, which is designed to address these challenges. It features a novel architecture that integrates the Convolutional Block Attention Module (CBAM) and Swin-Transformer to enhance global semantic understanding of feature maps and maximize the utilization of contextual information. Such integration significantly improves the accuracy with which small targets are detected against complex backgrounds. Additionally, we propose an improved detection network that combines the improved K-Means algorithm with a smooth Non-Maximum Suppression (NMS) algorithm. This network employs an adaptive dynamic K-Means clustering algorithm to pinpoint target areas of concentration in remote sensing images that feature varied distributions and uses a smooth NMS algorithm to suppress the confidence of overlapping candidate boxes, thereby minimizing their interference with subsequent detection results. The enhanced algorithm substantially bolsters the model's robustness in handling multi-scale target distributions, preserves more potentially valid information, and diminishes the likelihood of missed detections. This study involved experiments performed on the publicly available DIOR remote sensing image dataset and the DOTA aerial image dataset. Our experimental results demonstrate that, compared with other advanced detection algorithms, K-CBST YOLO outperforms all its counterparts in handling both datasets. It achieved a 68.3% mean Average Precision (mAP) on the DIOR dataset and a 78.4% mAP on the DOTA dataset. [ABSTRACT FROM AUTHOR]

The use of mobile robots in substations improves maintenance efficiency and ensures the personal safety of staff working at substations, which is a trend in the development of technologies. Strong electric and solid magnetic fields around high-voltage equipment in substations may lead to the breakdown and failure of inspection devices. Therefore, safe operation range measurement and coordinated planning are key factors in ensuring the safe operation of substations. This paper first summarizes the current developments that are occurring in the field of fixed and mobile safe operating range sensing methods for substations, such as ultra-wideband technology, the two-way time flight method, and deep learning image processing algorithms. Secondly, this paper introduces path-planning algorithms based on safety range sensing and analyzes the adaptability of global search methods based on a priori information, local planning algorithms, and sensor information in substation scenarios. Finally, in view of the limitations of the existing range awareness and path-planning methods, we investigate the problems that occur in the dynamic changes in equipment safety zones and the frequent switching of operation scenarios in substations. Furthermore, we explore a new type of barrier and its automatic arrangement system to improve the performance of distance control and path planning in substation scenarios. [ABSTRACT FROM AUTHOR]

Efficient traffic sign recognition is crucial to facilitating the intelligent driving of new energy vehicles. However, current approaches like the Vision Transformer (ViT) model often impose high storage and computational demands, escalating hardware costs. This paper presents a similarity filter pruning method based on locally linear embedding. Using the alternating direction multiplier method and the loss of the locally linear embedding method for the model training function, the proposed pruning method prunes the operation model mainly by evaluating the similarity of each layer in the network layer filters. According to the pre-set pruning threshold value, similar filters to be pruned are obtained, and the filter with a large cross-entropy value is retained. The results from the Belgium Traffic Sign (BelgiumTS) and German Traffic Sign Recognition Benchmark (GTSRB) datasets indicate that the proposed similarity filter pruning based on local linear embedding (SJ-LLE) pruning algorithm can reduce the number of parameters of the multi-head self-attention module and Multi-layer Perceptron (MLP) module of the ViT model by more than 60%, and the loss of model accuracy is acceptable. The scale of the ViT model is greatly reduced, which is conducive to applying this model in embedded traffic sign recognition equipment. Also, this paper proves the hypothesis through experiments that "using the LLE algorithm as the loss function for model training before pruning plays a positive role in reducing the loss of model performance in the pruning process". [ABSTRACT FROM AUTHOR]

Accurate segmentation of the pulmonary airway tree is crucial for diagnosing lung diseases. To tackle the issues of low segmentation accuracy and frequent leaks in existing methods, this paper proposes a precise segmentation method using quasi-spherical region-constrained wavefront propagation with tracheal wall gap sealing. Based on the characteristic that the surface formed by seed points approximates the airway cross-section, the width of the unsegmented airway is calculated, determining the initial quasi-spherical constraint region. Using the wavefront propagation method, seed points are continuously propagated and segmented along the tracheal wall within the quasi-spherical constraint region, thus overcoming the need to determine complex segmentation directions. To seal tracheal wall gaps, a morphological closing operation is utilized to extract the characteristics of small holes and locate low-brightness tracheal wall gaps. By filling the CT values at these gaps, the method seals the tracheal wall gaps. Extensive experiments on the EXACT09 dataset demonstrate that our algorithm ranks third in segmentation completeness. Moreover, its performance in preventing airway leaks is significantly better than the top-two algorithms, effectively preventing large-scale leak-induced spread. [ABSTRACT FROM AUTHOR]

This paper studies an LSTM-based adaptive transmission method for a 360-degree video and proposes a non-uniform encoding transmission strategy based on LSTM. Our goal is to maximize the user's video experience by dynamically dividing the 360-degree video into tiles of different numbers and sizes, and selecting different bitrates for each tile. This aims to reduce buffering events and video jitter. To determine the optimal number and size of tiles at the current moment, we constructed a dual-layer stacked LSTM network model. This model predicts, in real-time, the number, size, and bitrate of the tiles needed for the next moment of the 360-degree video based on the distance between the user's eyes and the screen. In our experiments, we used an exhaustive algorithm to calculate the optimal tile division and bitrate selection scheme for a 360-degree video under different network conditions, and used this dataset to train our prediction model. Finally, by comparing with other advanced algorithms, we demonstrated the superiority of our proposed method. [ABSTRACT FROM AUTHOR]

Currently, existing deep learning methods exhibit many limitations in multi-target detection, such as low accuracy and high rates of false detection and missed detections. This paper proposes an improved Faster R-CNN algorithm, aiming to enhance the algorithm's capability in detecting multi-scale targets. This algorithm has three improvements based on Faster R-CNN. Firstly, the new algorithm uses the ResNet101 network for feature extraction of the detection image, which achieves stronger feature extraction capabilities. Secondly, the new algorithm integrates Online Hard Example Mining (OHEM), Soft non-maximum suppression (Soft-NMS), and Distance Intersection Over Union (DIOU) modules, which improves the positive and negative sample imbalance and the problem of small targets being easily missed during model training. Finally, the Region Proposal Network (RPN) is simplified to achieve a faster detection speed and a lower miss rate. The multi-scale training (MST) strategy is also used to train the improved Faster R-CNN to achieve a balance between detection accuracy and efficiency. Compared to the other detection models, the improved Faster R-CNN demonstrates significant advantages in terms of mAP@0.5, F1-score, and Log average miss rate (LAMR). The model proposed in this paper provides valuable insights and inspiration for many fields, such as smart agriculture, medical diagnosis, and face recognition. [ABSTRACT FROM AUTHOR]

With the rapid advancement of autonomous driving technology, the recognition of vehicle lane-changing can provide effective environmental parameters for vehicle motion planning, decision-making and control, and has become a key task for intelligent vehicles. In this paper, an improved method for vehicle taillight detection and intent recognition based on YOLOv8 (You Only Look Once version 8) is proposed. Firstly, the CARAFE (Context-Aware Reassembly Operator) module is introduced to address fine perception issues of small targets, enhancing taillight detection accuracy. Secondly, the TriAtt (Triplet Attention Mechanism) module is employed to improve the model's focus on key features, particularly in the identification of positive samples, thereby increasing model robustness. Finally, by optimizing the EfficientP2Head (a small object auxiliary head based on depth-wise separable convolutions) module, the detection capability for small targets is further strengthened while maintaining the model's practicality and lightweight characteristics. Upon evaluation, the enhanced algorithm demonstrates impressive results, achieving a precision rate of 93.27%, a recall rate of 79.86%, and a mean average precision (mAP) of 85.48%, which shows that the proposed method could effectively achieve taillight detection. [ABSTRACT FROM AUTHOR]

Hole detection is an important means of crack detection for aero-engine blades, and the current technology still mainly relies on manual operation, which may cause safety hazards for visual reasons. To address this problem, this paper proposes a deep learning-based, aero-engine blade crack detection model. First, the K-means++ algorithm is used to recalculate the anchor points, which reduces the influence of the anchor frame on the accuracy; second, the backbone network of YOLOv5s is replaced with Mobilenetv3 for a lightweight design; then, the slim-neck module is embedded into the neck part, and the activation function is replaced with Hard Sigmoid for redesign, which improves the accuracy and the convergence speed. Finally, in order to improve the learning ability for small targets, the SimAM attention mechanism is embedded in the head. A large number of ablation tests are conducted in real engine blade data, and the results show that the average precision of the improved model is 93.1%, which is 29.3% higher; the number of parameters of the model is 12.58 MB, which is 52.96% less, and the Frames Per Second (FPS) can be up to 95. The proposed algorithm meets the practical needs and is suitable for hole detection. [ABSTRACT FROM AUTHOR]

This paper investigates null control within the transmit–receive beampattern of Frequency Diverse Array-Multiple-Input and Multiple-Output (FDA-MIMO) systems, presenting a novel phase-only optimization approach for achieving null control in FDA-MIMO. We employ an alternating multiplier framework, which transforms the intricate and inherent constant modulus constraint and numerous amplitude constraints in optimization into more manageable projection problems. By employing a phase-only optimization strategy, the intricate hardware and computational burdens associated with null control in FDA-MIMO are effectively alleviated. The simulation results indicate that the algorithm proposed in this paper exhibits excellent null control ability while precisely maintaining constant modulus constraints, and it possesses an extremely high computational efficiency. [ABSTRACT FROM AUTHOR]

This paper presents an analytical method for the dimensioning and selection of the four glass tube collector types: single-glazed with an air layer, single-glazed with a vacuum layer, double-glazed with an air layer, and double-glazed with a vacuum layer. In the first part of the paper (dimensioning phase), the iterative thermal resistance calculation algorithms were developed for all glass tube collector types, whereby the iterative thermal resistance calculation algorithm of the single-glazed tube collector with an air layer was experimentally tested and validated. The second part of the paper (selection phase) uses a multi-criteria decision-making method to determine the optimal glass tube collector design. Unlike other papers, three indicator groups are taken into account in this case: geometric (mass, surface occupation, total surface occupation, volume occupation), economic (manufacturing and exploitation costs), and ecological (embodied energy and greenhouse gas emission). The proposed method is characterized by simple and fast calculations with satisfactory accuracy, which avoids high investment costs (experimental research), approximation and discretization of physical models (numerical research), and a large number of input parameters with boundary conditions (theoretical research). It should be noted that, with certain additions and changes, it can also be applied to other solar thermal collectors, so the authors believe such tools are handy for the global scientific public. [ABSTRACT FROM AUTHOR]