Journal of Mechanical Engineering, Automation and Control Systems
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Research on the relationship between feature extraction time and training samples of hyperspectral image based on spatial domain
Get PDFHyperspectral image (HSI) feature extraction is an important means to improve the classification of different ground features. According to the structural characteristics of hyperspectral data, the general feature extraction scheme can extract features from the point of view of spectral dimension, spatial and spatial spectrum. And the feature extraction time is also an index to measure the feature extraction method. Therefore, from the perspective of spatial dimension, this paper explores the relationship between HSI feature extraction time and training sample ratio. Three groups of HSIs sets were used for correlation test and analysis in the experiment. According to the characteristics of different data sets, the best selection scheme between spatial domain feature extraction method and training samples is given
Effect of pier bearing construction on nearby high-speed rail line bridges
Get PDFIn order to analyze the impact of the new pier bearing platform construction on the bridge section adjacent to the high-speed railway line, a new pier bearing platform project adjacent to the high-speed railway line in the soft soil area of the Yangtze River Delta is taken as the background of the project, and the finite element software Plaxis 3D is used to study the impact of the pier bearing platform construction on the bridge section adjacent to the high-speed railway line, and to analyze the effect of distance on the horizontal displacement and settlement of the bridge foundation soil body. The neighboring high-speed rail line bridge is displaced horizontally toward the foundation, with the maximum horizontal displacement of 1.5 mm and the maximum settlement of 5.5 mm; the existing pier bearing platforms are also displaced in the same direction, with the maximum horizontal displacement of 2.7 mm and the maximum settlement of 5.4 mm, and the pier bearing platforms in the middle are affected the most
Unveiling the future of cardiac care: advances in mechanical circulatory support
Get PDFCongestive heart failure (CHF) is a multifaceted clinical syndrome characterized by the inability of the heart to pump blood effectively, leading to inadequate oxygen and nutrient delivery to the body tissues. Despite advancements in treatment strategies, including guideline-directed medical treatment (GDMT), end-stage CHF remains a significant cause of morbidity and mortality worldwide. Heart transplantation is considered to be the gold standard treatment of end stage CHF but constrained by the lack of organ donors, lengthening waitlists, and the negative side effects of lifelong immunosuppressive medications. Mechanical circulatory support (MCS) has emerged as a pivotal intervention for patients with end-stage CHF, serving as a bridge to recovery, transplantation, or destination therapy. The aim of this narrative review is to highlight the historical development of MCS, to assess the recent status of MCS device technology and discuss current challenges associated with complications of MCS that need to be solved in the future by device development. The history of MCS dates back to pioneering efforts in the 1960s, with significant progress in device development and utilization over decades. MCS devices, including left ventricular assist devices (LVADs), extracorporeal membrane oxygenation (ECMO), and artificial hearts, play a crucial role in providing circulatory support to patients with end-stage CHF. Recent advancements in MCS technology aim to decrease the device size, enhance blood compatibility, reduce thrombo-embolic complications, and prolong device durability and battery life and improve physiological performance of MCS. Continued research and innovation are essential to address these challenges and improve outcomes in patients with end-stage CHF. Artificial intelligence (AI) has emerged as a valuable tool in cardiovascular medicine to facilitate risk prediction, patient selection, and treatment optimization for MCS and heart transplantation. Despite these advancements, challenges persist in MCS device selection, resource allocation, and integration of AI into clinical practice. Continued research and innovation are essential to address these challenges and improve outcomes in patients with advanced heart failure
Energy storage technologies and their combinational usage in micro/mini unmanned aerial vehicles: a review
Get PDFAs the world population increases, energy consumption extensively increases in every field. Energy is a need in several applications, and depending on its importance, its production should be renewable and clean. In order to meet energy requirements sustainably nowadays various alternative energy resources and improvements are recommended in each sector. One of the environmental steps taken in the aviation industry is the improvement of the propulsion systems and the resources used. For this purpose, the use of electrical energy, a more sustainable option than the energy obtained from fossil fuels, is suggested. In order for electrical energy to be used efficiently, it must be stored. This article reviews energy storage technologies used in aviation, specifically for micro/mini Unmanned Aerial Vehicles (UAVs). Combinational energy storage technologies in hybrid propulsion system architectures and their individual usage in all-electric propulsion system architectures are examined. New-generation propulsion technologies are also evaluated and classified in detail
Mayfly optimization algorithm: a review
Get PDFThis paper gives a review on the bio-inspired optimization methodology known as mayfly (MA) algorithm in order to resolve issues in optimization techniques. It is a newly formed meta-heuristic optimization algorithm that focuses on the movements of masculine and feminine mayflies. It is encouraged from flying behaviour also the methods of mating in mayflies. With the help of a realistic-world separate flow planning issue along with the coupling behaviour in numerous objective optimizations, the performance of the mayfly algorithm (MA) is well evaluated. Some of the implementations of this algorithm are discussed in this paper: Bearing fault diagnosis based on the mayfly algorithm, optimizing the performance of PEMFC, Covid diagnosis, wind speed optimization, improving the scheduling of solar wind speed using mayfly optimization, detecting fault in the wind turbine gearboxes, patterning in the array antennas with the help of optimization and so on .One of the main advantages of the MA is that it combines the other optimization algorithms namely swarm optimization (PSO) with the evolutionary optimizations (GA). The motion of the mayflies that resemble nuptial dance model along with the arbitrary flight helps in the improvement of the stability within the exploration and exploitation methods. In addition, allows escape from the community peak. All the above work reviewed shows promising results from the algorithm. More work can be carried out using this algorithm in future
Fourier series method for the stability solution of simply supported thin beams on two-parameter elastic foundations of the Pasternak, Filonenko-Borodich, Hetenyi or Vlasov models
Get PDFThe analysis of stability problems of beams on two-parameter foundations (Bo2PFs) is an important part of their design for compressive loads. This work presents novel first principles derivation of the governing differential equations of elastic stability (GDES) of thin beams resting on two-parameter elastic foundations of the Pasternak, Filonenko-Borodich, Hetenyi or Vlasov models. The requirements of translational and rotational equilibrium of all the applied, reactive and internal forces on an infinitesimal segment of the Bo2PF and the laws of infinitesimal calculus were used to formulate the GDES as a fourth order ordinary differential equation (ODE) in terms of the transverse displacement function ux. The GDES is non-homogeneous in the presence of applied transverse load qx but homogeneous when qx vanishes. This study presents the Fourier series method (FSM) for solving the governing differential equation of stability (GDES) for the case of Dirichlet boundary conditions. The FSM has the advantage of amenability to differentiation, and integration due to the orthogonality properties of the sinusoidal functions. Implementation of the FSM by assuming the unknown function in the GDES as a Fourier series of infinite terms and the exploitation of orthogonalization simplifies the problem to an algebraic eigenvalue problem which is the characteristic buckling equation. The exact eigenvalues are found by algebraic solution of the buckling equation. The exact eigenvalues were used to find the exact buckling loads and the exact buckling load coefficients. The critical buckling load was found to correspond to the first buckling mode (n= 1), and is identical with previous solutions in the literature. Numerical calculations for the critical buckling load parameters Kcr were presented for the Bo2PF problem for values of the dimensionless foundation parameters k-1= 0, k-2= 0; k-1= 100, k-2= 0; k-1= 0, k-2= 1; k-1= 100, k-2= 100; k-1= 0, k-2= 2.5; k-1= 100, k-2= 2.5. The present solutions were compared with previous solutions for Kcr in the literature. The comparison shows that the present FSM results are identical with previous results obtained using various other methods such as Recursive Differentiation Method, Finite Element Method, Generalized Integral Transform Method (GITM) and Stodola-Vianello Iteration Method. The study has illustrated the effectiveness of the FSM for solving Bo2PFs
Fault diagnosis and identification of rotating machinery based on one-dimensional convolutional neural network
No full textThe paper focuses on two kinds of rotating machinery, miniature table drilling machine and automobile engine, as the research object. Traditional machine learning has the need for manual feature extraction, and is very dependent on expert diagnostic experience and expertise, but also has the disadvantages of low accuracy, low timeliness, low efficiency, etc. For the traditional rotating machinery fault diagnosis method is more based on the traditional machine learning model, this paper puts forward a one-dimensional convolutional neural network-based fault identification method. According to the characteristics of the miniature table drilling machine and the automobile engine which are not detachable, the corresponding faults are set up respectively, Vibration signals of the attitude sensor are obtained by using the signal collector, and the collected data are preprocessed, then the CNN model is built for fault identification, and the network structure is constantly optimized to obtain the optimal network model with high accuracy (up to 100 %) and robustness. The results show that the one-dimensional convolutional neural network model improves the fault recognition accuracy and reduces the cost compared with the traditional machine learning SVM model when the original signal is used as the input signal
Methods of detection and localization of the sources of noise and vibration on car gearboxes: a review
No full textOne of the primary sources of noise and vibration in automobiles is gearboxes. Shafts, gears, and bearings are the main causes of noise and vibration in vehicle gearboxes. Various studies have reported that vibrations’ root cause is bearing excitation. Besides bearing fatal defects or extreme structure resonance amplification, gear mesh is the primary source of high-frequency vibration and noise, even in newly built units. Gear damage detection is frequently crucial in automotive gearboxes and vehicle safety. Furthermore, vibrations caused by shaft imbalances, shaft misalignments, and other factors can cause noise and vibrations in the drivetrain's transfer path. In addition, the vibration of an automobile gearbox is closely related to poor design, construction quality, and production accuracy. This paper reviewed previous research and methods on car gearboxes for conventional vehicles. It was obvious that frequency analysis and order analysis were commonly used in noise and vibration analysis on car gearboxes. Envelope analysis is usually used to analyze bearing faults. Finally, rolling-element bearing diagnostic techniques were also reviewed
Multi-factor coupled thermal simulation of flat-panel digital PCR structure
No full textTo achieve uniform reaction temperature in flat-plate digital polymerase chain reaction (dPCR), we propose a multi-factor coupled thermal simulation method for the structure of flat-plate digital PCR using finite element analysis. This will help us obtain the optimal method for the structure of flat-plate dPCR. Thermal simulations were conducted to analyze the effects of forced air cooling, thermoelectric cooler (TEC) arrangement spacing, and heat-conducting plate thickness on the temperature uniformity of the flat-plate dPCR. The resulting isothermal surfaces and velocity magnitude vectors were used to summarize the impact of each factor. The study found that maintaining a mechanical fan speed of 3000 revolutions per minute (RPM) ±10 % during the heating period resulted in a 29.3 % reduction in the standard deviation of the temperature on the surface of the heat-conducting plate. Additionally, when the TEC spacing was between 2 mm and 3.5 mm, the standard deviation of the temperature on the plate's surface decreased by 87.1 % to 93.4 %. When the thickness of the thermal plate ranges from 3.5 mm to 4.5 mm, the standard deviation of the temperature on the surface of the thermal plate varies by approximately 0.006. The experimental results, obtained by sampling and analyzing the temperature on the surface of the thermal plate, are consistent with the simulation results. This proves that the method is informative in determining the structural parameters of the dPCR to enhance temperature uniformity
Researching low frequency vibration of automobile-robot
Get PDFAutomobile-robot (self-driving automobile) is being researched and developed vigorously. When the automobile-robot is moving on the road surface, the low frequency vibration excitation not only influences the ride comfort of the automobile-robot but also strongly affects the durability of the vehicle’s structures. To research the automobile-robot’s vibration in the low frequency region, a dynamic model of the vehicle is established to calculate the vibration equations in the time region. Based on the theory of the Laplace transfer function, the automobile-robot’s vibration equations in the time region are transformed and converted to the vibration equations in the frequency region. Then, the effect of the design parameters and operation parameters on the characteristic of the automobile-robot’s acceleration-frequency is simulated and analyzed to evaluate the ride comfort as well as the durability of the automobile-robot’s structures in the frequency region. The research results show that the design parameters of the stiffness, mass, and road wavelength remarkably affect the characteristic of the automobile-robot’s acceleration-frequency. To reduce the resonant amplitude of the acceleration-frequency in the vertical and pitching direction of the automobile-robot, the stiffness parameters of the automobile-robot's and tires should be reduced while the mass of the automobile-robot’s body should be increased. Additionally, the road’s roughness also needs to be decreased or the road’s quality needs to be enhanced to reduce the resonant amplitude of the automobile-robot’s acceleration-frequency