Slow Speed Research Articles

An improved ant colony algorithm based on Q-Learning for route planning of autonomous vehicle

In view of the problems existing in the path planning algorithms of unmanned vehicles, such as low search efficiency, slow convergence speed and easy to fall into the local optimal. Based on the characteristics of route planning for unmanned vehicles, this paper introduces Q-Learning into the traditional ant colony algorithm to enhance the learning ability of the algorithm in dynamic environment, so as to improve the overall efficiency of route search. By mapping pheromones into Q values in Q-learning, rapid search in complex environments is realized, and a collection-free path satisfying constraints is quickly found. The results of case analysis show that compared with the traditional ant colony algorithm and the improved ant colony algorithm considering reward and punishment factors, the improved ant colony algorithm based on Q-Learning can effectively reduce the number of iterations, shorten the path optimization time and path length and other performance indicators, and has many advantages in jumping out of the local optimal, improving the global search ability and improving the convergence speed, and has good adaptability and robustness in complex environments. It ensures the safety and stability of unmanned vehicles in complex environments.

Effects of walking speeds on lower extremity kinematic synergy in toe vertical position control: An experimental study.

This study aimed to investigate whether lower limb joints mutually compensate for each other, resulting in motor synergy that suppresses toe vertical position fluctuation, and whether walking speeds affect lower limb synergy. Seventeen male university students walked at slow (0.85 ± 0.04 m/s), medium (1.43 ± 0.05 m/s) and fast (1.99 ± 0.06 m/s) speeds on a 15-m walkway while lower limb kinematic data were collected. Uncontrolled manifold analysis was used to quantify the strength of synergy. Two-way (speed × phase) repeated-measures analysis of variance was used to analyze all dependent variables. A significant speed-by-phase interaction was observed in the synergy index (SI) (P  < .001). At slow walking speeds, subjects had greater SI during mid-swing (P  < .001), while at fast walking speeds, they had greater SI during early-swing (P  < .001). During the entire swing phase, fast walking exhibited lower SI values than medium (P  = .005) and slow walking (P  = .027). Kinematic synergy plays a crucial role in controlling toe vertical position during the swing phase, and fast walking exhibits less synergy than medium and slow walking. These findings contribute to a better understanding of the role of kinematic synergy in gait stability and have implications for the development of interventions aimed at improving gait stability and reducing the risk of falls.

Automatic loading method for robot guided by 3D vision for scattered and stacked shoe soles

This paper primarily introduces a method for the automated feeding of scattered stacked shoe soles using a 3D visual-guided robot. Initially, addressing issues like slow speed in pose estimation and poor robustness during the robot sorting and feeding process, we introduce an enhanced pose estimation algorithm. This algorithm combines the improved Super 4-Point Congruent Sets (Super 4PCS) with the Truncated Least Squares Semidefinite Relaxation (TEASER) algorithm, significantly boosting the speed and robustness of pose estimation during sole sorting, and achieving precise target pose estimation. Building upon this foundation, we present a sorting strategy for disordered stacked shoe soles. This strategy integrates spatial positional information of each sole, employing multi-objective decision-making and recognition algorithms to determine optimal grasping targets. Finally, an experimental platform for automated sole feeding is designed to validate the proposed method. The experimental results indicate that the pose estimation method proposed in this paper achieves an average distance error of 2.04 mm and an average angular error of 2.72°, with the robot’s average success rate in grasping reaching 97.08%. Moreover, the average processing time of the vision algorithm is only 1.34 s, demonstrating good efficiency, precision, and robustness. This method effectively meets the automated feeding needs of scattered and stacked shoe soles in the actual production processes of shoemaking enterprises.

Red-billed blue magpie optimizer: a novel metaheuristic algorithm for 2D/3D UAV path planning and engineering design problems

Numerical optimization, Unmanned Aerial Vehicle (UAV) path planning, and engineering design problems are fundamental to the development of artificial intelligence. Traditional methods show limitations in dealing with these complex nonlinear models. To address these challenges, the swarm intelligence algorithm is introduced as a metaheuristic method and effectively implemented. However, existing technology exhibits drawbacks such as slow convergence speed, low precision, and poor robustness. In this paper, we propose a novel metaheuristic approach called the Red-billed Blue Magpie Optimizer (RBMO), inspired by the cooperative and efficient predation behaviors of red-billed blue magpies. The mathematical model of RBMO was established by simulating the searching, chasing, attacking prey, and food storage behaviors of the red-billed blue magpie. To demonstrate RBMO’s performance, we first conduct qualitative analyses through convergence behavior experiments. Next, RBMO’s numerical optimization capabilities are substantiated using CEC2014 (Dim = 10, 30, 50, and 100) and CEC2017 (Dim = 10, 30, 50, and 100) suites, consistently achieving the best Friedman mean rank. In UAV path planning applications (two-dimensional and three − dimensional), RBMO obtains preferable solutions, demonstrating its effectiveness in solving NP-hard problems. Additionally, in five engineering design problems, RBMO consistently yields the minimum cost, showcasing its advantage in practical problem-solving. We compare our experimental results with three categories of widely recognized algorithms: (1) advanced variants, (2) recently proposed algorithms, and (3) high-performance optimizers, including CEC winners.

The Effect of the Symbolic Meaning of Speed on Implicit Timing

Abstract It has been demonstrated that the stimulus’s features, including size, brightness, numerosity, and loudness, can affect the perception of subjective and explicit time. But, in a daily life situation, actual events presumably involve an implicit processing of time rather than an explicit processing, with some studies suggesting that the presentation of emotional stimuli before the target stimulus influences implicit timing. The present study aims to test the implicit component of temporal processing that the symbolic meaning of speed might influence. We used a time foreperiod task in which participants were first presented with a warning signal recalling the meaning of fast or slow speed, followed by the target. Our study shows significant main effects of the presented image cue and foreperiod effect. We observed faster reaction time when the target was preceded by a faster image compared to a slow image and in dependence of the factor of weight. Based on this, we conclude the symbolic meaning of speed can affect implicit timing by altering how the brain interprets temporal data.

A voltage driven dual-mode MoSe2 photodetector with graphene as van der Waals contact

Abstract Two-dimensional (2D) molybdenum selenide (MoSe2) is promising for developing photodetectors for harvesting lights from UV to near infrared band, while high responsivity and fast response speed are difficult to be simultaneously realized. Herein, we present a dual-mode MoSe2 photodetector with asymmetric electrodes, in which Graphene and Cr metal are utilized as Ohmic and Schottky contacts, respectively. The photodiode possesses a fabulous Schottky characteristic with rectification ratio of ~250 and low dark current of ~40 pA at -1 V. Under forward bias voltage of 1 V, the photodetector works at photoconductive mode with slow response speed (decay time: ~5 minutes) but high responsivity (632 mA/W). However, at reverse bias voltage, the photodetector plays as a photovoltaic type device owing to the Schottky barrier between Cr and MoSe2. Because of the reinforced built-in electric field, the photodetector driven at -5 V shows much faster response speeds (rise time: 1.96 ms; decay time: 755 µs). The study provides a deep understanding of an asymmetric structure MoSe2 photodetctors operated at two modes, which promotes a forward step toward 2D material optoelectronics.&amp;#xD;

Fused filament fabrication using stainless steel 316L‐polymer blend: Analysis and optimization for green density and surface roughness

AbstractThis study is centered on the analysis of composite component additive manufacturing, via fused filament fabrication (FFF), comprising of 316L stainless steel particles in a polymer matrix. It examines the impact of several process factors of FFF, including extrusion speed, layer height, and extrusion temperature, on the density and surface roughness with the goal to minimize porosity. The independent and interaction impacts of these parameters were investigated using a central composite design technique. The technique of analysis of variance was utilized to determine the influence of relevant factors. Regression analysis was used to establish statistical models linking the parameters to green density and surface roughness. The green density was enhanced by reducing the layer height from 0.2 to 0.1 μm and decreasing the nozzle speed from 100 to 20 mm min−1. The surface roughness was improved by using a slow printing speed and minimum layer height. The ideal temperature range for producing favorable results in terms of green density and surface roughness during extrusion was found to be between 235 and 240°C. Significant correlations were found between the parameters and the green density and surface roughness. A genetic algorithm was used as an optimisation tool to determine the thresholds that would provide the highest green density and the lowest surface roughness values. The porosity of the samples generated with the optimized settings was evaluated using microtomography scans. The methodology presented here can be applied to composite and standard polymeric filament printing to determine optimal printing parameters.Highlights Blend of stainless steel 316L and polymer was used for fused filament fabrication. Printing speed and layer height were the dominating for density and roughness. Height 0.2 mm, speed 20 mm s−1, and temperature 239°C resulted 4.73 g cm−3 density and 21.09 μm roughness Microtomography revealed porosity of 1.7% in the optimized sample.

Dynamic CBCT imaging using prior model-free spatiotemporal implicit neural representation (PMF-STINR).

Dynamic cone-beam computed tomography (CBCT) can capture high-spatial-resolution, time-varying images for motion monitoring, patient setup, and adaptive planning of radiotherapy. However, dynamic CBCT reconstruction is an extremely ill-posed spatiotemporal inverse problem, as each CBCT volume in the dynamic sequence is only captured by one or a few X-ray projections, due to the slow gantry rotation speed and the fast anatomical motion (e.g., breathing). &#xD;Approach: We developed a machine learning-based technique, prior-model-free spatiotemporal implicit neural representation (PMF-STINR), to reconstruct dynamic CBCTs from sequentially acquired X-ray projections. PMF-STINR employs a joint image reconstruction and registration approach to address the under-sampling challenge, enabling dynamic CBCT reconstruction from singular X-ray projections. Specifically, PMF-STINR uses spatial implicit neural representations to reconstruct a reference CBCT volume, and it applies temporal INR to represent the intra-scan dynamic motion with respect to the reference CBCT to yield dynamic CBCTs. PMF-STINR couples the temporal INR with a learning-based B-spline motion model to capture time-varying deformable motion during the reconstruction. Compared with the previous methods, the spatial INR, the temporal INR, and the B-spline model of PMF-STINR are all learned on the fly during reconstruction in a one-shot fashion, without using any patient-specific prior knowledge or motion sorting/binning. &#xD;Main results: PMF-STINR was evaluated via digital phantom simulations, physical phantom measurements, and a multi-institutional patient dataset featuring various imaging protocols (half-fan/full-fan, full sampling/sparse sampling, different energy and mAs settings, etc.). The results showed that the one-shot learning-based PMF-STINR can accurately and robustly reconstruct dynamic CBCTs and capture highly irregular motion with high temporal (~0.1s) resolution and sub-millimeter accuracy.&#xD;Significance: PMF-STINR can reconstruct dynamic CBCTs and solve the intra-scan motion problem from conventional 3D CBCT scans without using any prior anatomical/motion model or motion sorting/binning. It can be a promising tool for motion management by offering richer motion information than traditional 4D-CBCTs.

Frailty in Patients on Dialysis.

Frailty is a condition that is frequently observed among patients performing dialysis. It is characterised by a decline in both physiological and cognitive state, leading to a combination of symptoms such as weight loss, exhaustion, low physical activity, weakness, and slow walking speed. Frail patients not only experience a poor quality of life, but they are also at a higher risk of hospitalization, infection, cardiovascular events, dialysis-associated complications, and death. Frailty occurs as a result of a combination and interaction of various medical issues in patients who are on dialysis. Unfortunately, there is no cure for frailty. To address frailty, a multifaceted approach is necessary, involving coordinated efforts from nephrologists, geriatricians, nurses, allied health practitioners, and family members. Strategies such as optimizing nutrition and CKD-related complications, reducing polypharmacy by deprescription, personalized dialysis prescription and considering home-based or assisted dialysis may help slow the decline of physical function over time in subjects with frailty. This review discusses the underlying causes of frailty in patients on dialysis and examines the methods and difficulties involved in managing frailty among this group.

Driver fatigue detection method based on facial features using deep learning

Fatigue driving can result in delayed driver reactions and lack of concentration, increasing the risk of traffic accidents. Therefore, finding effective fatigue detection methods is crucial for accident prevention and ensuring public safety. Traditional driver fatigue detection methods based on facial features are limited by their reliance on manually designed features and classifiers, leading to low accuracy, slow detection speed, and inefficient performance. Hence, methods based on deep learning have gradually become a trend in fatigue-driving detection research. This paper focuses on driver fatigue detection methods based on facial features using deep learning. In this study, we analyze numerous research outcomes on how to accurately extract, analyze, and integrate various facial features, such as eye features, mouth features, facial expressions, and head postures, to precisely determine the driver's fatigue state. Then, we compare the latest deep learning-based technologies. This paper provides an information-rich repository to help researchers select more promising designs, that can drive the widespread development of fatigue-driving detection technologies.

A Sinh–Cosh-Enhanced DBO Algorithm Applied to Global Optimization Problems

The Dung beetle optimization (DBO) algorithm, devised by Jiankai Xue in 2022, is known for its strong optimization capabilities and fast convergence. However, it does have certain limitations, including insufficiently random population initialization, slow search speed, and inadequate global search capabilities. Drawing inspiration from the mathematical properties of the Sinh and Cosh functions, we proposed a new metaheuristic algorithm, Sinh–Cosh Dung Beetle Optimization (SCDBO). By leveraging the Sinh and Cosh functions to disrupt the initial distribution of DBO and balance the development of rollerball dung beetles, SCDBO enhances the search efficiency and global exploration capabilities of DBO through nonlinear enhancements. These improvements collectively enhance the performance of the dung beetle optimization algorithm, making it more adept at solving complex real-world problems. To evaluate the performance of the SCDBO algorithm, we compared it with seven typical algorithms using the CEC2017 test functions. Additionally, by successfully applying it to three engineering problems, robot arm design, pressure vessel problem, and unmanned aerial vehicle (UAV) path planning, we further demonstrate the superiority of the SCDBO algorithm.

A Multi-Objective Optimization Problem Solving Method Based on Improved Golden Jackal Optimization Algorithm and Its Application

The traditional golden jackal optimization algorithm (GJO) has slow convergence speed, insufficient accuracy, and weakened optimization ability in the process of finding the optimal solution. At the same time, it is easy to fall into local extremes and other limitations. In this paper, a novel golden jackal optimization algorithm (SCMGJO) combining sine–cosine and Cauchy mutation is proposed. On one hand, tent mapping reverse learning is introduced in population initialization, and sine and cosine strategies are introduced in the update of prey positions, which enhances the global exploration ability of the algorithm. On the other hand, the introduction of Cauchy mutation for perturbation and update of the optimal solution effectively improves the algorithm’s ability to obtain the optimal solution. Through the optimization experiment of 23 benchmark test functions, the results show that the SCMGJO algorithm performs well in convergence speed and accuracy. In addition, the stretching/compression spring design problem, three-bar truss design problem, and unmanned aerial vehicle path planning problem are introduced for verification. The experimental results prove that the SCMGJO algorithm has superior performance compared with other intelligent optimization algorithms and verify its application ability in engineering applications.

GDPs-YOLO: an improved YOLOv8s for coal gangue detection

ABSTRACT In response to the issues of low detection accuracy, slow speed, excessively large models, and difficult deployment in existing coal gangue recognition algorithms, a coal gangue target detection network based on an inverted residual structure is proposed. By conducting in-depth research on advanced edge computing networks, DPsP structure and DsP structure have been devised in this paper, while incorporating GhostModule to construct the GDPs-YOLO network within the YOLOv8s. The experimental results demonstrate the superior performance of the GDPs-YOLO network compared to both the baseline network and the control network. In comparison with the YOLOv5 series, YOLOv8 series, and four advanced edge computing networks, an increase in detection accuracy for coal gangue targets by a maximum of 2.5%, 1.6%, and 3.3% is observed, respectively. The model simultaneously exhibits enhanced speed and reduced model size, with a speed increase ranging from 12.5% to 86.85% compared to the control network. The compressibility of the model ranges from 24.07% to 94%. The inference latency measures approximately 2.8 ms, while the image processing speed reaches around 357 images per second, thereby satisfying the requirements for real-time detection.

EResNet-SVM: an overfitting-relieved deep learning model for recognition of plant diseases and pests.

The accurate recognition and early warning for plant diseases and pests are a prerequisite of intelligent prevention and control for plant diseases and pests. As a result of the phenotype similarity of the hazarded plant after plant diseases and pests occur, as well as the interference of the external environment, traditional deep learning models often face the overfitting problem in phenotype recognition of plant diseases and pests, which leads to not only the slow convergence speed of the network, but also low recognition accuracy. Motivated by the above problems, the present study proposes a deep learning model EResNet-support vector machine (SVM) to alleviate the overfitting for the recognition and classification of plant diseases and pests. First, the feature extraction capability of the model is improved by increasing feature extraction layers in the convolutional neural network. Second, the order-reduced modules are embedded and a sparsely activated function is introduced to reduce model complexity and alleviate overfitting. Finally, a classifier fused by SVM and fully connected layers are introduced to transforms the original non-linear classification problem into a linear classification problem in high-dimensional space to further alleviate the overfitting and improve the recognition accuracy of plant diseases and pests. The ablation experiments further demonstrate that the fused structure can effectively alleviate the overfitting and improve the recognition accuracy. The experimental recognition results for typical plant diseases and pests show that the proposed EResNet-SVM model has 99.30% test accuracy for eight conditions (seven plant diseases and one normal), which is 5.90% higher than the original ResNet18. Compared with the classic AlexNet, GoogLeNet, Xception, SqueezeNet and DenseNet201 models, the accuracy of the EResNet-SVM model has improved by 5.10%, 7%, 8.10%, 6.20% and 1.90%, respectively. The testing accuracy of the EResNet-SVM model for 6 insect pests is 100%, which is 3.90% higher than that of the original ResNet18 model. This research provides not only useful references for alleviating the overfitting problem in deep learning, but also a theoretical and technical support for the intelligent detection and control of plant diseases and pests. © 2024 Society of Chemical Industry.

A Static Security Region Analysis of New Power Systems Based on Improved Stochastic–Batch Gradient Pile Descent

The uncertainty in the new power system has increased, leading to limitations in traditional stability analysis methods. Therefore, considering the perspective of the three-dimensional static security region (SSR), we propose a novel approach for system static stability analysis. To address the slow training speed of traditional deep learning algorithms using batch gradient descent (BGD), we introduce an improved stochastic–batch gradient descent (S-BGD) search method that combines the advantages of stochastic gradient descent (SGD) in fast training. This method ensures both speed and precision in parameter training. Moreover, to tackle the problem of getting trapped in local optima and saddle points during parameter training, we draw inspiration from kinematic theory and propose a gradient pile (GP) training method. By utilizing accumulated gradients as parameter corrections, this method effectively avoids getting stuck in local optima and saddle points, thereby enhancing precision. Finally, we apply the proposed methods to construct the static security region for the IEEE-118 new power system using its data as samples, demonstrating the effectiveness of our approach.

Trajectory Tracking Control of Mobile Manipulator Based on Improved Sliding Mode Control Algorithm

Research on trajectory tracking control for climbing welding robots holds significant importance in the field of automated welding. However, existing trajectory tracking methods suffer from issues such as jitter and slow speed. In this paper, an improved sliding mode control strategy is proposed based on the self-designed wall-climbing welding mobile manipulator. Firstly, a new adaptive sliding mode control strategy is proposed for the mobile platform based on the kinematic model. By introducing a new approach law, the controller is designed when the distance between the center of mass is unknown. Secondly, regarding the manipulator, we analyze simplified dynamic equations, extract uncertain components, and utilize a CNN for compensation. This compensation strategy is integrated into the sliding mode control law, achieving precise control over the manipulator and effectively resolving issues like slow tracking speeds, large errors, and chattering. The stability of the robot control system is proved by the Lyapunov function. Through simulation analysis and experimental validation, the proposed control method is confirmed to be feasible and superior.

Development of the Extrusion Based Food 3D Printer Machine by Modifying Fused Deposition Modelling (FDM) 3D Printer

The additive manufacturing (AM) technique is the method to objectify a design by adding the material layer-by-layer. AM for food 3D printing application is used to modify the process of food production in aspects of shape, color, flavor, texture, and nutrition. However, the price of food 3D printers available in the market is too high compared to the benefits they can provide. Addressing this issue, modifying the traditional fused deposition modelling (FDM) 3D printer can be a cheaper alternative that offers more advantages for food production process. This study provides the modification of the FDM 3D printer, such as the designing, fabricating, and setting the new extrusion mechanism to develop the food 3D printer. The modifications are mainly in the extrusion mechanism where the printing material changes from filament into semi-fluid food material. The proposed food 3D printer then tested by printing two different using semi-fluid food materials, i.e., strawberry, and peanut jam. In addition, two different shapes, three different dimensions, and two travel speeds are being selected for the printing test, i.e., 40 and 60 mm/s. As a result, the FDM 3D printer was modified and converted successfully into a food 3D printer. Based on the printing outcomes, it was showed that the strawberry jam has a better surface finish than peanut jam due to its texture consistency. Moreover, the strawberry material also has a lower percentage of error particularly when printed at slower speed. This research is expected to contribute to the development of affordable food 3D printers in Indonesia.

A Fast Adaptive Millimeter-Wave Radar Clustering Algorithm

This paper focuses on the intelligent recognition problem of radar detection targets. Aiming at the low accuracy and slow speed of millimeter-wave radar clustering point cloud information, a feature algorithm of millimeter-wave radar suitable for detecting targets is proposed. In the detection of targets by millimeter-wave radar, distance is the biggest factor affecting the number and degree of sparsity. A method that combines the feature information of the point cloud with the KD tree proximity search algorithm and the DBSCAN clustering algorithm is proposed, which can adapt to the problems of uneven target point cloud, small amount of data and slow clustering speed. The improved algorithm can use the KD tree to quickly find adjacent points and calculate the distance between adjacent points. The corresponding number of thresholds is set according to the distance where the target is located, and the radius of the target area reflected by the millimeter-wave radar plus the distance of the last threshold point is used as the neighborhood radius of the improved algorithm. Therefore, fast and adaptive parameter adjustment of the millimeter-wave radar can be realized. Simulation tests show that the improved clustering algorithm has better parameters. The accuracy of the improved algorithm is increased by 4.2%, and it also greatly improves the clustering speed.

Lightweight object detection network for multi‐damage recognition of concrete bridges in complex environments

AbstractTo solve the challenges of low recognition accuracy, slow speed, and weak generalization ability inherent in traditional methods for multi‐damage recognition of concrete bridges, this paper proposed an efficient lightweight damage recognition model, constructed by improving the you only look once v4 (YOLOv4) with MobileNetv3 and fused inverted residual blocks, named YOLOMF. First, a novel lightweight network named MobileNetv3 with fused inverted residual (MobileNetv3‐FusedIR) is constructed as the backbone network for YOLOMF. This is achieved by integrating the fused mobile inverted bottleneck convolution (Fused‐MBConv) into the shallow layers of MobileNetv3. Second, the standard convolution in YOLOv4 is replaced with the depthwise separable convolution, resulting in a reduction in the number of parameters and complexity of the model. Third, the effects of different activation functions on the damage recognition performance of YOLOMF are thoroughly investigated. Finally, to verify the effectiveness of the proposed method in complex environments, a data enhancement library named Imgaug is used to simulate concrete bridge damage images under challenging conditions such as motion blur, fog, rain, snow, noise, and color variations. The results indicate that the YOLOMF shows excellent multi‐damage recognition proficiency for concrete bridges across varying field‐of‐view sizes as well as complex environmental conditions. The detection speed of YOLOMF reaches 85f/s, facilitating effective real‐time multi‐damage detection for concrete bridges under complex environments.

[Brain magnetic resonance image registration based on parallel lightweight convolution and multi-scale fusion].

Medical image registration plays an important role in medical diagnosis and treatment planning. However, the current registration methods based on deep learning still face some challenges, such as insufficient ability to extract global information, large number of network model parameters, slow reasoning speed and so on. Therefore, this paper proposed a new model LCU-Net, which used parallel lightweight convolution to improve the ability of global information extraction. The problem of large number of network parameters and slow inference speed was solved by multi-scale fusion. The experimental results showed that the Dice coefficient of LCU-Net reached 0.823, the Hausdorff distance was 1.258, and the number of network parameters was reduced by about one quarter compared with that before multi-scale fusion. The proposed algorithm shows remarkable advantages in medical image registration tasks, and it not only surpasses the existing comparison algorithms in performance, but also has excellent generalization performance and wide application prospects.