Journal of Mechanical Engineering, Automation and Control Systems
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The influence of robots on the spatial electric field measurement for zero value insulator recognition
Get PDFZero value insulators pose a threat to the safe and stable operation of transmission lines. By walking with tracked robots, the local electric field distribution of insulator strings can be quickly detected and measured, thereby identifying zero value pieces. To clarify the influence of robot architecture on the electric field measurement of insulator strings and propose a fast identification criterion for zero-value recognition, this paper establishes a true model of 220 kV insulator strings and an equivalent model of robots. The electric field distribution characteristics of the robot structure working on insulator strings were analyzed through finite element simulation, and the influence of factors such as robot material and size on local electric field distortion characteristics was studied, especially the local electric field variation laws under zero and non-zero values. The model's validity is confirmed through relevant simulations, ensuring its reliability for practical applications. Further detailed simulation analysis was conducted on the local electric field distortion characteristics of the robot architecture at different positions of the insulator string, and the electric field measurement characteristics of the zero value insulator were obtained. Based on the simulation results of the 220 kV insulator string, a criterion for measuring and identifying zero value insulators for 500 kV was proposed and applied to 500 kV. The research results reveal the influence of electric field detection and measurement robots on local electric field distortion of zero value insulators, which can provide technical support for intelligent operation and maintenance of external insulation in power transmission and distribution
Research on converter transformer state early warning system based on confidence ratio-EEMD and multi-cascade network
Get PDFAiming at the problems of poor prediction effect of non-stationary parameters and single warning rule of UHV converter transformer, this study proposes an intelligent warning method based on decomposition-multi-level cascade network and fuzzy set. Firstly, the integrated empirical modal decomposition technique is used to decompose the target parameter sequence into multiple sub-sequences, and the effective components are screened by the DPR-KLdiv confidence ratio, which is dynamically grouped and reconstructed to form a multilevel feature input; and the multilevel cascade network is constructed by combining multi-device parameters to make the time series prediction. The fuzzy function is further introduced to establish the parameter state mapping rules to expand the alarm triggering conditions. The experiments are validated by actual equipment data, and the local discharge signals of different defects are detected by ultra-high frequency method to enhance the generalization ability of the parameters. The results show that the average RMSE and MAE of this method are 23.21 and 18.47 respectively under the hours step prediction, and the accuracy of the warning is over 90 %, which effectively improves the accuracy of non-smooth parameter prediction and the flexibility of the warning decision
Artificial intelligence-based stock market price prediction, a review
Get PDFThe stock market, a cornerstone of the global financial system, is characterized by its dynamic and volatile nature, which makes accurate price-trend prediction challenging. However, traditional statistical models often fail to capture this complexity. Recent advancements in Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), have transformed stock market forecasting by using diverse datasets and algorithms. This review examines recent studies on AI methodologies for stock market price trend prediction models by analyzing architectures, datasets, performance metrics, and limitations, with a focus on hybrid models, sentiment analysis, and dataset diversity. Hybrid approaches, including the Multi-Model Generative Adversarial Network Hybrid Prediction Algorithm (MMGAN-HPA), K-means long short-term memory (LSTM), and LSTM autoregressive output (LSTM-ARO), improve predictive accuracy by combining statistical methods with deep learning. Sentiment analysis models such as Stock Senti WordNet (SSWN) and Hybrid Quantum Neural Network (HQNN) integrate social media sentiment to capture market dynamics. Real-time frameworks that use stream processing show promise for high-frequency trading applications. This review addresses key challenges including data noise, nonstationarity, overfitting risks, and black-box model interpretability. Solutions include GAN-based synthetic data generation, transformer-based architectures such as SpectralGPT, and optimization techniques for computational efficiency. This review provides a taxonomy of AI-based approaches, while identifying gaps for future research. These findings highlight the potential of AI in financial forecasting while emphasizing the need for interdisciplinary collaboration to address its limitations in data quality, methodology, interpretability, and ethics
Impact of underground near surface ore body mining on the stability of overburden and dangerous rock masses
Get PDFIn order to explore the impact of near surface ore body mining on the stability of overburden and surface dangerous rock masses, a Phosphate Mine was used as the engineering background. On-site investigation method was adopted to clarify the stability conditions of the surface dangerous rock. Numerical analysis software was used to simulate the evolution laws of overburden deformation, stress, and plastic zone. The research results indicate that the development of interlayer structural planes in the surrounding rock of the roof of the mining area can easily cause the collapse of the roof slab or sheet. The strata are hard and brittle in lithology, with developed rock fractures. Dangerous rock blocks are formed under the combination of fissures and rock layers. The mining disturbance generated during the mining process is relatively small. The impact on the rock layers, adjacent mining sites, and surface stability is weak. The surface is less affected by the mining of underground ore bodies and has not reached the maximum allowable value. Under the condition of first mining the ph1# ore body and then mining the ph2# ore body, the displacement of the overburden is relatively small. There is no distribution of connectivity in the plastic zone in the mining pillars, mining areas, and overburden. The research results can provide theoretical reference for the feasibility analysis of near surface ore body mining in similar mines
Fuzzy algorithm-based active control method for vibration of a mechanical gear transmission system
No full textThe detached raft automatic frequency isolation system is a complicated system with high exceptionally nonlinear, high electromagnetic, and multi-source vibration modes. However, it generates a statistical method and it is hard to operate the organization. The fuzzy control algorithm, as an astute control method, can give a keen path to the active management of a complicated system of floating rafts. This study uses a system identification approach to construct mathematical models for a floating raft active vibration isolation system with discrete transfer work. The fuzzy model is used in tests and simulations controller is built using two contributions of acceleration and its variation, as well as a single result of control voltage. The control isolation system is a complicated system with many moving parts. A lot of moving parts profoundly nonlinear, high electromagnetic and multi-source vibration modes, generating a statistical method and it is hard to operate the organization. The fuzzy control algorithm, as a smart control method, can give a keen path to the active management of a sophisticated floating raft system. This research uses an identification strategy to construct a floating raft active vibration isolation technology discrete transfer work mathematical models. The fuzzy controller is then put together using two contributions: acceleration and variation, as well as a single outcome of control voltage for simulations and experiments research
Modal analysis and optimization design of ultra-high acceleration platform rail frame
No full textThe ultra-high acceleration macro and micro motion platform has the advantages of high positioning accuracy and small error, and the key mechanism rail frame of the ultra-high acceleration macro and micro motion platform is optimized based on modal analysis to achieve the performance optimization of the platform. SolidWorks software was used to build the rail frame model, and ANSYS Workbench software was used to carry out modal analysis, topology optimization and response surface optimization, etc., so as to reduce the quality of the rail frame as much as possible under the premise of maintaining the stability of the first-order natural frequency. The results show that the optimization of the response surface meets the expected goal, the first-order natural frequency of the guide rail frame increases by 0.7 %, the mass decreases from 6.165 kg to 5.592 kg, and the change rate is 9.2 %, which achieves the purpose of lightweight
Investigation of dynamic response characteristics of light fixed-wing aircraft
No full textIn order to ensure the stability of light fixed-wing aircraft during flight missions, considering the effects of relative airflow velocity and angle of attack, the distribution characteristics of velocity and pressure fields under different conditions, as well as the law of change of dynamic parameters, were derived by using aerodynamic methods. In the free modal condition, the modal truncation method was used to simulate and analyze the low-order modal shapes. Based on the modal analysis results, the sweep frequency range was set to 3-50 Hz, with a step size of 1.6 Hz, for a total of 30 substeps. A harmonic load of 1500 N was applied to the fuselage, and the displacement-frequency response curves and stress-frequency response curves of the fuselage structure and wing structure were extracted after the calculation. The results shows that the maximum lift-drag ratio occurs when the angle of attack is 6°, and the peak displacement deformation of the aircraft occurred around 24 Hz
Analysis and optimization of abnormal noise in lubricating oil circuit of diesel engine
No full textEngine abnormal noise is one of the common engine faults, which will affect the comfort, power and safety of the automobile at the same time. In order to study the abnormal noise existing in the test process of diesel engine lubricating oil circuit system, 1D numerical simulation analysis of its flow field is carried out by using simulation software GT-power to verify the boundary conditions of the model and analyze the fluctuation of pressure at different speeds and temperatures. Through numerical analysis, it is found that there is almost no pressure fluctuation in the lubrication system before the pressure limiting valve is opened, but after the pressure limiting valve is opened, pressure fluctuation occurs in the pipeline, and the pressure fluctuation is different at different positions. Finally, the lubrication pipeline is simulated and analyzed by Pumplinx software, and the pressure fluctuation in the lubrication pipeline of diesel engine is reduced by optimizing the diameter of the oil pipeline and increasing the cavity structure
Enhancing non-destructive testing in concrete structures: a GADF-CNN approach for defect detection
Get PDFThis research introduces a novel approach for detecting defects in concrete structures. It utilizes the Gramian Angular Difference Field (GADF) in combination with a Convolutional Neural Network (CNN) enhanced by depthwise separable convolutions and attention mechanisms. The key contribution of this work is the use of GADF to transform one-dimensional impact-echo signals into two-dimensional images, thereby improving feature extraction and computational efficiency for analysis by the CNN. This advancement offers a new perspective in non-destructive testing technologies for concrete infrastructure. Comprehensive evaluation on a varied dataset of concrete structural defects reveals that our GADF-CNN model achieves an impressive test accuracy of 98.24 %, surpassing conventional models like VGG16, ResNet18, DenseNet, and ResNeXt50, and excelling in precision, recall, and F1-score metrics. Ultimately, this study enhances the integration of sophisticated image transformation techniques with deep learning, contributing to safer and more durable concrete infrastructure, and represents a noteworthy development in the field
Low-level laser therapy parameters in the treatment of Orofacial pain in temporomandibular disorder
Get PDFTemporomandibular disorder (TMD) comprises a group of dysfunctions of the masticatory system involving joint, myofascial, and even sensory alterations. The main symptom of TMD is orofacial pain, which has a negative impact on the quality of life of TMD sufferers, affecting their productivity at work and school, the quality of their sleep, their appetite, and their satisfaction with life. An alternative for the treatment of TMD is the low-level laser therapy (LLLT), which stimulates cellular functions and promotes therapeutic effects. However, there is no consensus in the literature. Objective: This review aimed to elucidate the parameters of low-level laser therapy in the treatment of orofacial pain in patients with temporomandibular disorder. Methods: A search was carried out for scientific articles published between 2013 and 2023 in the MEDLINE, PEDro, LILACS and SciELO databases. Results: A total of 21 studies were previously selected and after the eligibility criteria, 10 studies were included. Can be suggest a protocol for treating patients with temporomandibular disorder by low-level laser therapy with the following parameters: infrared wavelength, energy density less than 10 J/cm2, output power from 100 to 250 mW and at least 8 sessions of treatment. Conclusion: The low-level laser therapy improves orofacial pain in patients with temporomandibular disorder