Speed Requirements Research Articles

Justification of the application of a distributed network of photoelectric converters to power a linear motor of magnetolevitation transport

Modern high-speed transport is the basis of sustainable economic and social development of the state, society in compliance with environmental requirements. The concept of power supply of a linear motor of magneto-levitation transport from a distributed network of photovoltaic converters is substantiated. The basic power element of a track power plant is proposed in the form of a completed unit consisting of a solar panel, a storage device and an inverter, which operates on a load in the form of a "short" track coil. The use of a "short" track coil allows reducing electrical energy losses, since the traction force is formed only in the zone of interaction with the rolling stock, energy is not transmitted to unused sections of the track structure. By reducing the length of the working section, other energy indicators of the system as a whole are significantly suspended, in particular the power factor and effi-ciency. Reducing the length of the working sections in conditions of high speeds of rolling stock will lead to increased requirements for reliability and speed of operation of track switches. The track switches are created on the basis of power semiconductor switches, which are controlled by modern microcontrollers. The effectiveness of the proposed structure de-pends on the solar activity in the region of the transport artery. The estimated solar activity indicators allow us to state that the weight indicators of the rolling stock that must move on the track with a "short" section correspond to the available resources.

Dynamic Robot Routing and Destination Assignment Policies for Robotic Sorting Systems

Robotic sorting systems (RSSs) use mobile robots to sort items by destination. An RSS pairs high accuracy and flexible capacity sorting with the advantages of a flexible layout. This is why several express parcel and e-commerce retail companies, who face heavy demand fluctuations, have implemented these systems. To cope with fluctuating demand, temporal robot congestion, and high sorting speed requirements, workload balancing strategies such as dynamic robot routing and destination reassignment may be of benefit. We investigate the effect of a dynamic robot routing policy using a Markov decision process (MDP) model and dynamic destination assignment using a mixed integer programming (MIP) model. To obtain the MDP model parameters, we first model the system as a semiopen queuing network (SOQN) that accounts for robot movement dynamics and network congestion. Then, we construct the MIP model to find a destination reassignment scheme that minimizes the workload imbalance. With inputs from the SOQN and MIP models, the Markov decision process minimizes parcel waiting and postponement costs and helps to find a good heuristic robot routing policy to reduce congestion. We show that the heuristic dynamic routing policy is near optimal in small-scale systems and outperforms benchmark policies in large-scale realistic scenarios. Dynamic destination reassignment also has positive effects on the throughput capacity in highly loaded systems. Together, in our case company, they improve the throughput capacity by 35%. Simultaneously, the effect of dynamic routing exceeds that of dynamic destination reassignment, suggesting that managers should focus more on dynamic robot routing than dynamic destination reassignment to mitigate temporal congestion. Funding: This work was supported by The Fundamental Research Funds for the Central Universities [Grant WK2040000094] and the National Natural Science Foundation of China [Grants 71921001 and 72091215/72091210]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2023.0458 .

Adaptive Prescribed Time Control Method for Automatic Carrier Landing System

To address the issue of the short landing time and high convergence speed requirements for carrier-based aircraft, an adaptive prescribed time control (APTC) method is proposed. This method combines adaptive technique to estimate and compensate for modeling uncertainties and disturbances in the landing system. It ensures custom-defined prescribed time convergence for glide path tracking errors, regardless of initial states and control parameters. The stability of the landing control method is proven using the Lyapunov function theorem. Based on this method, an Automatic Carrier Landing System (ACLS) for carrier-based aircraft is developed. Numerical simulation tests with different convergence times verify that the method can achieve custom-defined prescribed time convergence. Finally, by introducing the PID and traditional prescribed time control methods for comparison, the numerical results demonstrate that the proposed APTC method achieves faster convergence speed and higher convergence accuracy in landing trajectory tracking.

Study on the Customization of Robotic Arms for Spray-Coating Production Lines

This paper focuses on the design and development of a customized 7-axis suspended robotic arm for automated spraying production lines. The design process considers factors such as workspace dimensions, workpiece sizes, and suspension positions. After analyzing degrees of freedom and workspace coordinates, 3D modeling ensures the arm can reach designated positions and orientations. Servo motors and reducers are selected based on load capacity and speed requirements. A suspended body method allows flexible use within the workspace. Kinematics analysis is conducted, followed by trajectory-tracking experiments using the manifold deformation control method. Results from simulation and real experiments show minimal error in tracking, demonstrating the effectiveness of the control method. Finally, the actual coating thickness sprayed by the 7-axis suspended robotic arm at four locations on the motorcycle shell was measured. The results show that the measured values at each location fall within the standard range provided by the manufacturer, demonstrating consistency in spraying across different regions. This consistency highlights the precision and effectiveness of the robotic arm’s control system in achieving uniform coating thickness, even on complex and curved surfaces. Therefore, the robotic arm has been successfully applied in a factory’s automated spraying production line.

LW-YOLO11: A Lightweight Arbitrary-Oriented Ship Detection Method Based on Improved YOLO11.

Arbitrary-oriented ship detection has become challenging due to problems of high resolution, poor imaging clarity, and large size differences between targets in remote sensing images. Most of the existing ship detection methods are difficult to use simultaneously to meet the requirements of high accuracy and speed. Therefore, we designed a lightweight and efficient multi-scale feature dilated neck module in the YOLO11 network to achieve the high-precision detection of arbitrary-oriented ships in remote sensing images. Firstly, multi-scale dilated attention is utilized to effectively capture the multi-scale semantic details of ships in remote sensing images. Secondly, the interaction between the spatial information of remote sensing images and the semantic information of low-resolution features of ships is realized by using the cross-stage partial stage. Finally, the GSConv module is introduced to minimize the loss of semantic information on ship features during transmission. The experimental results show that the proposed method has the advantages of light structure and high accuracy, and the ship detection performance is better than the state-of-the-art detection methods. Compared with YOLO11n, it improves 3.1% of mAP@0.5 and 3.3% of mAP@0.5:0.95 on the HRSC2016 dataset and 1.9% of mAP@0.5 and 1.3% of mAP@0.5:0.95 on the MMShip dataset.

HDNLS: Hybrid Deep-Learning and Non-Linear Least Squares-Based Method for Fast Multi-Component T1ρ Mapping in the Knee Joint.

Non-linear least squares (NLS) methods are commonly used for quantitative magnetic resonance imaging (MRI), especially for multi-exponential T1ρ mapping, which provides precise parameter estimation for different relaxation models in tissues, such as mono-exponential (ME), bi-exponential (BE), and stretched-exponential (SE) models. However, NLS may suffer from problems like sensitivity to initial guesses, slow convergence speed, and high computational cost. While deep learning (DL)-based T1ρ fitting methods offer faster alternatives, they often face challenges such as noise sensitivity and reliance on NLS-generated reference data for training. To address these limitations of both approaches, we propose the HDNLS, a hybrid model for fast multi-component parameter mapping, particularly targeted for T1ρ mapping in the knee joint. HDNLS combines voxel-wise DL, trained with synthetic data, with a few iterations of NLS to accelerate the fitting process, thus eliminating the need for reference MRI data for training. Due to the inverse-problem nature of the parameter mapping, certain parameters in a specific model may be more sensitive to noise, such as the short component in the BE model. To address this, the number of NLS iterations in HDNLS can act as a regularization, stabilizing the estimation to obtain meaningful solutions. Thus, in this work, we conducted a comprehensive analysis of the impact of NLS iterations on HDNLS performance and proposed four variants that balance estimation accuracy and computational speed. These variants are Ultrafast-NLS, Superfast-HDNLS, HDNLS, and Relaxed-HDNLS. These methods allow users to select a suitable configuration based on their specific speed and performance requirements. Among these, HDNLS emerges as the optimal trade-off between performance and fitting time. Extensive experiments on synthetic data demonstrate that HDNLS achieves comparable performance to NLS and regularized-NLS (RNLS) with a minimum of a 13-fold improvement in speed. HDNLS is just a little slower than DL-based methods; however, it significantly improves estimation quality, offering a solution for T1ρ fitting that is fast and reliable.

Inherent stochasticity, noise and limits of detection in continuous and time-gated fluorescence systems

We present a model for the noise and inherent stochasticity of fluorescence signals in both continuous wave (CW) and time-gated (TG) conditions. When the fluorophores are subjected to an arbitrary excitation photon flux, we apply the model and compute the evolution of the probability mass function (pmf) for each quantum state comprising a fluorophore’s electronic structure, and hence the dynamics of the resulting emission photon flux. Both the ensemble and stochastic models presented in this work have been verified using Monte Carlo molecular dynamic simulations that utilize the Gillespie algorithm. The implications of the model on the design of biomolecular fluorescence detection systems are explored in three relevant numerical examples. For a given system, the quantum-limited signal-to-noise ratio (QSNR) and limits of detection are computed to demonstrate how key design tradeoffs are quantified. We find that as systems scale down to micro- and nano- dimensions, the interplay between the fluorophore’s photophysical qualities and use of CW or TG has ramifications on optimal design strategies when considering optical component selection, measurement speed, and system energy requirements. While CW systems remain a gold standard, TG systems can be leveraged to overcome cost and system complexity hurdles when paired with the appropriate fluorophore.

ДЕРЕКТЕРДІ КӨРІНЕТІН ЖАРЫҚ АРҚЫЛЫ БЕРУГЕ БОЛАТЫН МОДУЛЯЦИЯ ӘДІСТЕРІН ЗЕРТТЕУ

The emergence of new directions based on the optical range in the field of communications is explained by existing problems in organizing modern wireless infrastructure. Currently, the radio frequency band widely used for data transmission is overloaded and cannot meet the rapidly growing traffic and demand for wireless data services. Additional implementation of wireless systems based on the use of optical range can help solve existing problems. Visible light communication, as an integral part of optical wireless communication, can realize the requirements for data transmission speed in the network, and reduce the load on the bandwidth of existing wireless networks. The article discusses modulation methods in a wireless optical communication network. Modulation characteristics that are important for system performance are studied. For types of pulse modulation, the power spectral density is calculated and studied to obtain time waveforms with a given signal-to-noise ratio. A model of an ideal transmission and reception system for OOC-NRZ in the presence of additive white Gaussian noise is constructed, which is suitable for indoor visible light data transmission systems. Signal shapes and the influence of instrumental and external noise were studied. Calculated values of the bit error probability based on experimental data are given. The proposed simulation model can be used in the design of real transmitting and receiving devices for a visible light communication system.

Inline Measurement of Light Beam Induced Current (LBIC) Under High-Injection and Reverse Bias Conditions

Quality control in solar cell production relies on characterization methods that are fast enough to yield information on the properties of each solar cell in less than about one second. Imaging techniques such as electroluminescence (EL) are well established methods revealing spatially resolved quality mappings. Scanning techniques such as light beam induced current (LBIC) are common laboratory methods yielding complementary information but do not meet the speed requirements. In our work, we analyse an inline implementation of the LBIC method which is extended with respect to its measurement parameters, i.e., both the laser power and a solar cell bias-voltage are varied. As these extended conditions differ from conventional LBIC-applications at zero bias and low injection, we compare our inline-LBIC images with EL images and conventional LBIC images by means of an image contrast analysis. We find that our method resembles more the EL imaging as variations in the local series resistance are prominently detected. Thus, the proposed LBIC approach with extended measurement parameter range can yield both local short circuit information and local series resistance information. With our setup, measurement speeds around 700 ms are achieved.

Rapid Coupling Analysis Model of Thermal Protection and Thermal Control for Electronic Equipment Cabin of High Speed Vehicles

Abstract In view of the serious problem of aerothermal “penetration” effect and equipment thermal accumulation effect in the electronic equipment cabin under long endurance and high speed flight conditions, this paper establishes thermal network models for thermal protection analysis and thermal control analysis based on lumped parameter principle, and verifies the validity of the thermal network models. Combined with thermal protection analysis model and thermal control analysis model, the thermal network model for coupling analysis of thermal protection and thermal control of electronic equipment cabin is established. And the necessity of establishing the coupling analysis model of thermal protection and thermal control is demonstrated. The thermal network model for coupling analysis of thermal protection and thermal control established in this paper takes only tens of seconds to analyse a single condition, and can well meet the requirements of calculation accuracy and calculation speed in the scheme stage of the high-speed vehicle. The research results are of great significance for the rapid iterative design of thermal management scheme in the scheme stage of high-speed vehicles.

Research on ZYNQ neural network acceleration method for aluminum surface microdefects

Convolutional Neural Networks (CNN) are an important means of detection of microdefects on the aluminum surface, and the high complexity and computing power requirements of the CNN model lead to difficulties in deploying them on edge computing platforms as the detection accuracy continues to improve. We have studied a lightweight acceleration method for detecting microdefects on aluminum surfaces on the Zynq-7000 All Programmable SoC (ZYNQ) platform. A lightweight aluminum surface defect detection network (LADFastDet) and high-performance accelerators based on ZYNQ are designed to meet the requirements of precision and speed under limited resources. In the LADFastDet structure, a lightweight inverted residual block is designed by combining depthwise convolution, inverted residual block, and inverted bottleneck. A multiscale feature fusion structure is designed to effectively improve the detection accuracy of LADFastDet, especially small target defects. We design accelerators on ZYNQ through optimization methods such as loop optimization strategy, ping-pong buffering, and multichannel and multiple interfaces data reading and writing to reduce data access latency and thus improve the computing speed. The experimental results show that the LADFastDet model has a mAP of 97.51%, the inference time of the accelerators for a single image is 42.57 ms, and a power consumption of 2.15 W, which achieves a throughput of 24.9 GOPS and an energy efficiency of 11.58 GOPS/W.

ACCELERATING IMAGE PROCESSING USING PARALLEL COMPUTATIONS IN OPENMP: DEVELOPMENT AND PERFORMANCE ANALYSIS OF A GRAPHICS EDITOR

In today’s digital world, image processing plays a crucial role in various aspects of human life. From creating visual content for social media to analyzing medical images, powerful tools for image manipulation are in constant demand. The growing requirements for image quality and processing speed make the search for efficient methods to accelerate these processes highly relevant. This paper investigates the development and implementation of a graphics editor that utilizes OpenMP parallel computing to accelerate data processing. This technology enables the efficient distribution of computational tasks across multiple processor cores, significantly improving system performance. The developed software offers a set of popular graphic effects, including negative, grayscale, sepia, blur, sharpening, and posterization. Each effect is implemented in both sequential and parallel modes using OpenMP, allowing for the comparison of different computational approaches. To evaluate the effectiveness of the proposed solution, a series of experiments were conducted on images of various sizes. These experiments involved applying each effect to images ranging from small icons to high-quality photographs. A comparative analysis of the efficiency of sequential and parallel computation methods demonstrated the significant advantages of the latter. The results of the study show a substantial acceleration of image processing when using OpenMP technology. This acceleration is particularly noticeable for computationally intensive effects such as blurring or sharpening, and when working with large images. In some cases, it was possible to achieve a significant increase in processing speed, opening up new possibilities for working with large volumes of graphic data. This research has significant practical value for software developers working on optimizing the performance of graphics editors and other image processing applications. It demonstrates how the application of modern parallel computing technologies can significantly improve the efficiency of working with graphic data, paving the way for the creation of more powerful and faster image processing tools.

A Human Pose Estimation Method Based on the BlazePose Model

Objective: This study aims to evaluate the performance of the BlazePose model in human pose estimation for sports actions to improve the accuracy and computational efficiency of the model. Methods: The research employed a variety of datasets for training and evaluating the model, investigating the generalization capability of the model under different experimental conditions. Detection and tracking were carried out through the two-stage mechanism of the BlazePose model, identifying the human figure contours and initially predicting the key points, and then refining the positions of the key points through the tracking module. Different parameters such as model complexity and key point smoothing were set, and the steps including image preprocessing, feature extraction, key point detection, naming, and skeleton construction were conducted. Results: The research outcomes yielded the two-dimensional keypoints of single-frame human body images and the construction of 3D coordinate models, and obtained the visualization line charts of PCK experimental data. Through the comparison of PCK scores of different models, it was concluded that on the basketball Dataset, the performance of BlazePose was superior to that of OpenPose, particularly in terms of FPS performance. Although the PCK score of the lightweight version of BlazePose was slightly lower, its FPS performance was higher, making it suitable for scenarios with high requirements for speed. Furthermore, BlazePose effectively reduced the model complexity by offering models of different complexities and using occlusion simulation in training, without sacrificing too much accuracy. Conclusion: This research presents the efficacy of lightweight neural networks in real-time human pose estimation and, through further experimental analyses, investigates the possibilities of enhancing their performance advantages and application effects.

Unbalance detection of threshing cylinder in non-stationary rotation based on variational mode extraction

The rotating speed of the threshing cylinder was hardly constant in practice due to transmission error and cylinder speed would change with feed quantity to ensure the grain threshing effect. This paper proposed a novel unbalance detection method for non-stationary rotation. Variational mode extraction (VME) was introduced to extract unbalanced vibration signal and the unbalance feature index (UFI) was constructed as a criterion of weighting factor selection. Moreover, the unbalance phase calculation method and phase reference in non-stationary were proposed, which could eliminate the influence of speed change. The advantages of VME and UFI were verified by simulation, and the effectiveness of proposed method was proved by experiment on multi-cylinder test rig and combine harvest. This method could realize the reconstruction of unbalanced vibration signal in time-varying speed with low computational cost, break the strict requirement of constant speed of the existing balancing method.

A trajectory tracking control method for the discharge arm of the self-propelled forage harvester

The cooperative operation between the self-propelled forage harvester and the trailer aims to achieve precise and automatic forage unloading. The discharge arm serves as the core structure for conveying forage, and the precision and speed of its motion control are crucial factors influencing the efficiency of forage harvesting. In this context, we proposed a trajectory tracking control method for the discharge arm based on an improved particle swarm optimization (IPSO)-PID controller. Firstly, we utilized the Denavit-Hartenberg (D-H) model of the discharge arm for a positive kinematics solution and the geometric resolution and linear fitting method for the inverse kinematics solution. Secondly, we employed the polynomial interpolation method for trajectory planning on the joint space of the discharge arm, and the PSO algorithm for time-optimal trajectory planning. Then, we designed an IPSO-PID trajectory tracking control algorithm and built an Amesim-Simulink co-simulation model for multiple simulation experiments. Finally, we conducted several performance tests of the discharge arm automatic control system in the workstation and the field, respectively. The experiment results indicate that the performance of the IPSO-PID controller exceeds that of all other controllers, which can meet the discharge arm’s motion control accuracy and speed requirements. Our research results are of great significance for improving the productivity and automation process of the self-propelled forage harvester and provide valuable references for research on automatic and precise control of material loading in other agricultural cooperative harvesting.

The Identification, Separation, and Clamp Function of an Intelligent Flexible Blueberry Picking Robot

Identifying fruit maturity accurately and achieving damage-free harvesting are challenges in designing blueberry-picking robots. This paper presents an intelligent flexible picking system. First, we trained a deep learning-based YOLOv8n network to locate the position of the fruit and determine fruit ripeness. We used a neural network to establish the relationship between fruit hardness and shape parameters, achieving an adaptive gripping force for different fruits. To address the issue of dense clusters in some blueberry varieties, we designed a fruit separation subsystem using a combination of flow field analysis and pressure-sensitive experiments. The results show that the mean average precision can reach 84.62%, the precision is 94.49%, the recall is 83.85%, the F1 score is 88.85%, and the test time is 0.12 s, which can meet the requirements for blueberry fruit recognition accuracy and speed. The spacing between closely packed fruits can increase by 4 mm, and the damage-free picking rate exceeds 92%, achieving stable, damage-free harvesting.

Detection and assessment of post-earthquake functional building ceiling damage based on improved YOLOv8

Ceiling is one of the crucial non-structural components in public buildings, but it is susceptible to damage after earthquakes. For important functional buildings, rapid assessment of their safety, economic losses, and recoverability is crucial post-earthquake. However, the current detection and assessment of ceiling damage in earthquake-stricken areas heavily relies on manual efforts. Due to limitations in detection methods and technological levels, these manual efforts struggle to meet the requirements of speed, accuracy, and safety. In response to these challenges, this study proposes a post-earthquake functional building ceiling damage detection and assessment method based on GTS-YOLOv8. Firstly, a dataset containing 1059 real ceiling damage images is established, categorized into two damage types: “Panels Fallen" (PF) and “Complete Destruction" (CD). Next, Ghost convolution is introduced, combined with Swin Transformer blocks, and a novel Slim Neck structure is designed to enhance the YOLOv8 algorithm. The GTS-YOLOv8 object detection model is presented. On the established dataset, GTS-YOLOv8 achieves 92 % mAP50 and 72.2 % mAP50-95, surpassing the baseline YOLOv8 by 2.3 % and 4.1 %, respectively. The study investigates ceiling damage detection under different lighting conditions and shooting distances. The results indicate that the highest detection accuracy is achieved under well-lit conditions and when the shooting perspective covers the entire ceiling area. Finally, based on the research outcomes, a detection and assessment architecture is constructed, and a corresponding system is developed, capable of automatically determining the damage State based on ceiling damage detection results. This architecture provides a foundation for the rapid assessment of building safety, economic losses, and recoverability post-earthquake.

Research on ultra-clean micro gas flow calibration technology of passive piston type with sealing

Aiming at the portable measurement and calibration needs of ultra-clean micro gas flow meters in the semiconductor industry, this paper designs an ultra-clean micro gas flow standard device based on passive piston type with sealing. The device adopts a horizontal structure, using the O-ring on the piston for radial sealing, unlike the traditional mercury and gap sealing, this design avoids the stringent requirements for piston speed and clearance. To increase automation of the calibration process, an automatic calibration system is built. Mathematical modeling and 6DOF dynamic mesh analysis are used to ensure that the piston operates at a stable stage. Through the uncertainty analysis, the extended uncertainty of the device reaches 0.21 % (k = 2). The uncertainty was verified by normalizing the deviation En and the results show that it is feasible to trace the clean gas flow rate with the piston standard device.

Application of Traffic Cone Target Detection Algorithm Based on Improved YOLOv5.

To improve the automation level of highway maintenance operations, the lightweight YOLOv5-Lite-s neural network was deployed in embedded devices to assist an automatic traffic cone retractor in completing recognition and positioning operations. The system used the lightweight shuffle Net network as a backbone for feature extraction, replaced convolutional layers with focus modules to reduce computational complexity, and reduced the use of the C3 layer to increase network speed, thereby meeting the speed and accuracy requirements of traffic cone placement and retraction operations while maintaining acceptable model inference accuracy. The experimental results show that the network could maintain recognition accuracy and speed values of around 89% and 9 fps under different working conditions such as varying distances, lighting conditions, and occlusions, meeting the technical requirements for deploying and retrieving cones at a speed of 30 cones per minute when the operating vehicle's speed was 20 km/h. The automatic traffic cone placement and retraction system operated accurately and stably, achieving the application of machine vision in traffic cone retraction operations.

Enhancing Efficiency Through Hydrogen-Powered Hyperloop Capsules: AI-Assisted Route Planning in Accordance with Circular Economy Principles

Abstract This study investigates the feasibility of integrating Hyperloop technology with hydrogen fuel systems. The primary objective was to evaluate how Hyperloop infrastructure can incorporate hydrogen refueling stations to optimize transport efficiency and sustainability. The research utilized simulation models developed with ArchiCAD and FlexSim software to analyze the integration of Hyperloop cargo and passenger transport with hydrogen refueling processes. The study included detailed simulations of Hyperloop operations and refueling logistics, focusing on safety, efficiency, and the potential for reducing carbon emissions. Key parameters such as capsule speed, refueling times, and infrastructure requirements were analyzed. Results indicated that Hyperloop technology can significantly enhance transport efficiency, particularly for high-value and time-sensitive goods, while hydrogen refueling stations offer a sustainable energy solution with considerable potential for reducing environmental impact. The findings demonstrate that integrating hydrogen refueling stations into the Hyperloop system can improve operational efficiency and sustainability. However, challenges remain in optimizing refueling processes and infrastructure development. Future research should address these challenges and explore additional AI-based optimization techniques to enhance the overall system performance. This research is especially useful for urban planners, transportation engineers, and policymakers aiming to develop efficient, sustainable transportation systems that incorporate cutting-edge technology like Hyperloop and hydrogen refueling infrastructure.