Journal of Mechanical Engineering, Automation and Control Systems
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Study on the interaction state between polymer modifiers and asphalt based on precise grinding
Get PDFTo clarify the impact of resin modifier fineness on the performance and interaction of modified asphalt, this study selects resin modifiers to prepare modified asphalts. The effects of the fineness parameters of resin modifiers on the road performance of modified asphalts are investigated. The segregation tests and Han curves are employed to analyze the influence of modifier on the compatibility of modified asphalt. The scanning electron microscopy is utilized to characterize the interaction between resin modifiers and asphalt. The results indicate that resin modifiers improve the high-temperature performance and deformation resistance of asphalt binder but lead to the adverse effect on low-temperature performance. Adjusting the particle size of the modifier could improve the modification effect of resin modifiers on asphalt binder
The technology of non-stop passage of high-speed passenger and freight trains on double-track sections and its impact on operational performance
Get PDFCurrently, a mixed system of high-speed passenger and freight trains has been implemented on the railways of Uzbekistan. During the movement of high-speed passenger trains on double-track main line sections, the movement of freight trains at all stations and segments is temporarily suspended for a specific period. From this perspective, in the present time, the suspension of freight train traffic is leading to numerous technical and economic expenses. In this article, based on experimental runs using the technology of passing freight trains without stopping, train movement schedules have been drawn up, and train operations have been organized without unnecessary stops. This research paper provides a restructured overview of the introduction and practical implementation of non-stop train operation technology for double-track railway lines where both high-speed passenger and freight trains operate simultaneously. Using real operational schedules, comparative experimental charts were developed to evaluate the new approach. The outcomes of this analysis demonstrate how the proposed technology influences efficiency and key performance indicators of train movements. A regression-based analytical model was also constructed to determine the relation between freight train waiting time and average section speed, ensuring reliability through statistical verification. Furthermore, the application of innovative solutions and technologies mentioned in this article to sections of high-speed highways creates an opportunity to increase transport transit potential and improve economic indicators
Modeling of the transportation process on the Kokand-Andijan section of the Kokand regional railway track junction of the Uzbek railway
Get PDFThe article presents original research results on the substantiation of the forward motion parameters of a freight train with a fixed maximum mass of the train and the main traction and operational characteristics of the energy efficiency of O’z-EL type AC freight electric locomotives on a real flat section of the railway. Energy-optimal control modes for the movement of the aforementioned freight train by electric locomotives of the O’z-EL series have been developed using the original computer hardware and software complex KORTES, and their traction and energy characteristics are presented in the form of numerical values and graphs with an error of no more than five percent compared to the practical data of the Kokand locomotive depot of the Uzbek Railway. The above results will be further used by the authors to evaluate the effectiveness of various options for energy-optimal control modes for the power equipment of the Oʼz-EL series electric locomotives when implementing freight transportation on sections of the Uzbekistan railway industry of varying complexity under real operating conditions
The current state and challenges of population mobility in the Republic of Uzbekistan
Get PDFThis article is devoted to the analysis of the public transport systems’ coverage ratio of the central cities of the Republic of Uzbekistan except the capital city. During the research the population density and public transport network have been analysed by comparing the coverage ratio
Self-supervised CNN for user behavior analysis on smart meter data
Get PDFSmart meters generate extensive data on individual consumer electricity usage, providing valuable insights that can aid in identifying demographic information and advancing the development of smart grids. Current research has primarily focused on traditional machine learning approaches for this task, with relatively few studies exploring deep learning methods, despite their potential for more accurate and efficient analysis. To address this gap, this paper proposes a self-supervised deep learning approach based on Convolutional Neural Network (CNN) to identify demographic information from smart meter data. The model leverages the Fast Fourier Transform (FFT) to detect frequency cycles within the dataset, which are then used to optimize the sizes of convolutional kernels. This design enhances periodic stability during shallow feature extraction, improving the model’s ability to capture meaningful patterns in the data. Furthermore, the model incorporates a self-supervised pre-training strategy to predict temporal and spatial interactions in load signals, effectively enhancing representation learning without relying on extensive labeled data. This approach ensures the model’s robustness and adaptability to different datasets. Comprehensive experiments were conducted on a publicly available Irish dataset to evaluate the model’s performance. Results demonstrate that the proposed model surpasses a series of state-of-the-art (SOTA) methods, achieving superior performance in demographic information identification. These findings highlight the effectiveness of integrating FFT-based kernel design and self-supervised learning in improving feature extraction and representation learning for smart meter data
Improved cubature Kalman filter for vehicle state estimation and measurement
Get PDFThe accurate estimation of vehicle state parameters has a significant impact on the active safety system of automobiles. Accurately obtaining vehicle operating parameters is the foundation and prerequisite for active safety control of vehicles. In response to the limited estimation accuracy of the traditional CKF method, the CKF was extended to fifth-order according to the third-order sphere-radius cubature rule, making it have the accuracy of fifth-order Taylor series expansion. At the same time, singular value decomposition was used instead of traditional Cholesky decomposition to form a fifth-order cubature Kalman filter (SVD-FCKF) estimator for singular value decomposition. Then, the SVD-FCKF was validated using the Carsim and Matlab/Simulink joint simulation platform. Finally, the effectiveness of the proposed method was verified through virtual experiments. The results show that the improved SVD-FCKF estimator can effectively improve the accuracy and stability of vehicle state estimation, with overall estimation performance better than the CKF estimator and has strong adaptability under high and low adhesion coefficient conditions. The research results can provide theoretical support for the active safety research of intelligent vehicles and have practical application value
Development of a flexible piezoresistive sensor prototype using resin doped with magnetically oriented nanoparticles
Get PDFHigh-performance flexible piezoresistive sensors are highly useful in areas such as biomedicine, soft robotics, and pressure change detection technology. However, they require complex designs and advanced manufacturing methods. In this study, the design and fabrication of a flexible piezoresistive sensor using a flexible resin matrix doped with magnetically oriented iron nanoparticles is presented. The sensor consists of a flexible polymer resin matrix as substrate, reinforced with iron nanoparticles in different concentrations (0.5 %, 0.7 % and 1 % by weight), oriented by a magnetic field during the manufacturing process. The nanoparticles significantly enhance the piezo-resistive properties of the sensor, increasing its sensitivity and electrical conductivity under compressive loads. The sensor demonstrated high sensitivity under loads greater than 100 N in samples with concentrations of 0.7 % and 1 % of nanoparticles, and exhibited stability during cyclic testing, demonstrating durability. Additionally, stability tests showed excellent durability in repeated load cycles. Scanning Electron Microscopy (SEM) and Confocal Laser Scanning Microscopy (CLSM) confirmed the effective alignment and distribution of the nanoparticles within the matrix, enhancing conductivity. This flexible piezoresistive sensor doped with nanoparticles has great potential for future applications in technologies such as soft robotics and electronic skins, where high sensitivity and durability in pressure detection are required
Analysis of modal and vibration response characteristics of high-pressure storage tanks
Get PDFAnalysis of dynamic characteristic was conducted focusing on the transportation of high-pressure storage tanks, covering two scenarios: independent transportation and mixed transportation. For independent transportation, analysis of free modal was carried out to obtain the first four orders of modal shapes. Additionally, the influence of two constraint methods on the modal characteristics and stress distribution was studied, including fixed at both ends and fixed at the cylinder body. Results show that when the tank was fixed at the cylinder body, it had a higher natural frequency and a lower stress level, making it safer. For mixed transportation, a finite element model was built for 6 high-pressure storage tanks, and analysis of random vibration was performed. The results showed that stress was mainly concentrated on the crossbeams and connection nodes, while the stress on the main body of the storage tanks was relatively low. The overall structure exhibited excellent fatigue performance and met the mechanical and safety requirements under random vibration conditions
PSO-PPO-based reinforcement learning control strategy for active suspension systems under multiple operating conditions
Get PDFTo address the poor generalization capability and extended training duration of reinforcement learning (RL)-based active suspension control systems, this study proposes a PSO-PPO algorithm for multiple operating condition suspension control. The methodology initiates with establishing a 4-DOF suspension dynamic model under three characteristic driving conditions: constant-speed operation, vehicle launch, and emergency braking, which is subsequently converted into state-space representation. The novel PSO-PPO framework synergizes particle swarm optimization with proximal policy optimization to train condition-specific agents. Based on the trained optimal agents, the entropy weight method is applied to adjust the reward function weight coefficients to develop a generalized multi-condition controller. Finally, the control effectiveness of the PSO-PPO algorithm is validated through constant-speed, launch, emergency braking, and multi-condition concatenated scenarios. Simulation results demonstrate that the PSO-PPO algorithm achieves shorter training times while maintaining balanced performance in ride comfort, handling stability, and safety across all conditions
Vibration characteristics testing and vibration reduction optimization design of four-wheel-drive micro-tiller handlebar assembly
Get PDFMicro-tillers are essential for agricultural operations in hilly and mountainous regions, yet their severe vibrations pose significant health risks to operators, including hand-arm vibration syndrome. This study presents an innovative vibration reduction solution through the installation of a damping spring isolator at the handle-frame connection point. Comprehensive vibration testing revealed that the vertical vibration under tillage conditions reached 2.15 m/s2 RMS, with spectral analysis identifying critical excitation frequencies at 39 Hz, 78 Hz, and 156 Hz. Constrained modal analysis demonstrated that the handle frame's third-order natural frequency of 41.02 Hz risked resonance with the engine’s 39 Hz excitation. The optimized isolator system, designed with a damping ratio of ξ= 0.2, successfully reduced this critical frequency to 34.87 Hz (15 % reduction), effectively avoiding resonance. Field validation showed significant vibration attenuation, with RMS values decreasing by 14.17 % (idle), 17.61 % (no-load), and 23.26 % (tillage), while achieving 19.3 % vibration energy absorption during operation. This research represents the first successful integration of isolation and damping mechanisms for micro-tiller handle frames, providing a cost-effective solution (< 1.5 % of machine cost) that significantly improves operator comfort and addresses long-standing ergonomic challenges in small-scale agricultural machinery. The solution's simple implementation without structural modifications makes it particularly suitable for widespread adoption in developing regions