Publications

State-of-charge estimation and charge equalization for electric agricultural machinery using Square-Root Central Difference Kalman Filter

Accurate State-of-Charge (SOC) estimation and reasonable charge equalization for power batteries is important for prolonging their lifetime. In the application of electric agricultural machinery, the SOC estimation of Ni-MH power batteries was implemented by the Square-Root Central Difference Kalman Filter (CDKF) approach in this paper. To provide charge equalization for all the battery strings in the test module, a relay matrix module was used for the power converter and the equalization method was based on the estimated SOC of the battery strings. The test results confirm that the SOC obtained from the Square-Root CDKF method can converge to the true biases much quicker in comparison with the EKF method. The experimental data illustrate that the battery strings are balanced well at the end of the charge equalization mode.

Analysis of Characteristics of the Lightning around the Capsized “Oriental Star” Incident

For the study of characteristics of the lightning around the capsized "Oriental Star" incident, this paper analyzes the lightning space distribution in the surrounding, preliminary judging from characteristics of the lightning figure out the form of disastrous weather patterns.It is concluded that: the wreck accident happened in lightning highest frequency period, also most lightning frequency region, as well as the biggest negative rather than positive lightning current time bucket and region.The latest lightning is less than 300 m from the accident, leads to a higher possibility of damage to the ship communications equipment by lightning electromagnetic induction, which needs to further strengthen the safety precautions against ships during a thunderstorm.

Computer-aided alignment method for large and complex optical systems

A computer-aided alignment method is put forward in this paper. It combines laser shearing interferometry and computer- aided optimization, accurately measures the alignment of the optical elements of the system being aligned and gives the adjustment project to control the wavefront quality. The theoretical analysis and experiments demonstrate the ability and effectiveness of this computer-aided alignment method and it especially suits for the on-site alignment of large and complex optical system.

Symmetry in Structural Health Monitoring

In this Special Issue on symmetry, we mainly discuss the application of symmetry in various structural health monitoring. For example, considering the health monitoring of a known structure, by obtaining the static or dynamic response of the structure, using different signal processing methods, including some advanced filtering methods, to remove the influence of environmental noise, and extract structural feature parameters to determine the safety of the structure. These damage diagnosis methods can also be effectively applied to various types of infrastructure and mechanical equipment. For this reason, the vibration control of various structures and the knowledge of random structure dynamics should be considered, which will promote the rapid development of the structural health monitoring. Among them, signal extraction and evaluation methods are also worthy of study. The improvement of signal acquisition instruments and acquisition methods improves the accuracy of data. A good evaluation method will help to correctly understand the performance with different types of infrastructure and mechanical equipment.

Back Analysis of Thermal Characteristics Parameters of Concrete and Its Application

A back analysis model is established for analyzing the thermal characteristics parameters of concrete, and an optimized back analysis process is programmed by using FEM simulative calculation and fixable tolerance method. Numerical examples shows the reliability of the calculation method, and it indicates a relatively rapid convergence. A project example is given in the paper for illustration. The obtaining of thermal characteristics parameter of concrete by optimized back calculation on the basis of site temperature observation values will become one of the effective way to partially or totally replace the parameters obtaining only from laboratory experiments.

Numerical investigation of the flow-induced vibration of a rotation pipe with internal flow

Reliability analysis of bridges under different loads using polynomial chaos and subset simulation

Abstract Reliability analysis of bridges is essential for the design of civil engineering structures. The classical methods, such as Monte Carlo Simulation (MCS) and subset simulation techniques, may provide accurate results. However, since the finite element model of the large‐scale civil engineering structures usually consists of a large number of degrees of freedom, structural reliability analysis of such structures is time‐consuming and computational intensive, which may restrict the use of these methods. This paper proposes a novel method for the reliability analysis of bridge under different types of loads by combining Polynomial Chaos (PC) and subset simulation techniques. The surrogate model of the limit state function is approximated by using the PC expansion, in which the PC coefficients are obtained from the least‐squares method. The subset simulation with a PC‐based surrogate model is then used to estimate the rare failure probability. Reliability analyses of bridge structures under different loading types, that is, static load, dynamic moving load, and seismic load are performed. Studies by using the MCS method and the classical subset simulation are also conducted, and the results from the proposed approach are compared with those from MCS and subset simulation to demonstrate the accuracy and efficiency of the proposed method.

Numerical Analysis of the Pit Excavation Influence on Foundation Piles

Excavation of the soil is actually the unloading process. The unloading process of soil makes the soil uplift, and the soil uplift is restricted by the pit engineering piles. And then, engineering piles should bear the vertical upwards pulling force caused by soil uplift. When pulling force is huge and tensile bearing capacity of pile is low, engineering piles could be pulled apart, and it is harmful to bearing capacity and durability of engineering piles. This paper adopts the finite element method to simulate engineering piles during pit excavation, and studies the traits of engineering piles in various excavation working conditions and different position. The computational results show that the deeper pit, the bigger displacement and tensile forces of piles, and the farther away from edges of the pit, the bigger displacement and forces of piles. In addition, the paper also gives a formula about the relationship between depth of pit excavation and uplift force on piles.

Damage identification of steel bridge based on data augmentation and adaptive optimization neural network

With the advancement of deep learning, data-driven structural damage identification (SDI) has shown considerable development. However, collecting vibration signals related to structural damage poses certain challenges, which can undermine the accuracy of the identification results produced by data-driven SDI methods in scenarios where data is scarce. This paper introduces an innovative approach to bridge SDI in a few-shot context by integrating an adaptive simulated annealing particle swarm optimization-convolutional neural network (ASAPSO-CNN) as the foundational framework, augmented by data enhancement techniques. Firstly, three specific types of noise are introduced to augment the source signals used for training. Subsequently, the source signals and augmented signals are recombined to construct a four-dimensional matrix as the input to the CNN, while defining the damage feature vector as the output. Secondly, a CNN is constructed to establish the mapping relationship between the input and output. Then, an adaptive fitness function is proposed that simultaneously considers the accuracy of SDI, model complexity, and training efficiency. The ASAPSO is employed to adaptively optimize the hyperparameters of the CNN. The proposed method is validated on an experimental model of a three-span continuous beam. It is compared with four other data-driven methods, demonstrating good effectiveness and robustness of SDI under cases of scarce data. Finally, the effectiveness of this SDI method is validated in a real-world case of a steel truss bridge.

An innovative electrical neurostimulation approach to mimic reflective urination control in spinal cord injury models

<title>Abstract</title> Neurogenic lower urinary tract dysfunction (NLUTD) is a frequent consequence of spinal cord injury (SCI), leading to symptoms that significantly impact quality of life. However, existing treatment strategies for managing NLUTD exhibit limitations and drawbacks. The demand for a novel, effective approach to restore bladder function and re-establish urination control. In this study, we introduce a new electrical neuromodulation strategy involving electrical stimulation of the major pelvic ganglion (MPG) to initiate bladder contraction, in conjunction with innovative programmable (IPG) electrical stimulation on the pudendal nerve (PN) to induce external urethral sphincter (EUS) relaxation in freely moving or anesthetized SCI mice. Furthermore, we conducted the void spot assay and cystometry coupled with EUS electromyography (EMG) recordings to evaluate voiding function and monitor bladder pressure and EUS muscle activity. Our findings demonstrate that our novel electrical neuromodulation approach effectively triggers coordinated bladder detrusor contraction and EUS relaxation, effectively counteracting SCI-induced NLUTD. Additionally, this electrical neuromodulation method enhances voiding efficiency, closely resembling natural reflexive urination in SCI mice. Thus, our study offers a promising electrical neurostimulation approach aimed at restoring physiological coordination and potentially offering personalized treatment for improving voiding efficiency in individuals with SCI-associated NLUTD.

Structural damage detection using low-rank matrix approximation and cointegration analysis

Development and application of random forest technique for element level structural damage quantification

This paper presents element level structural damage quantification using an ensemble-based machine learning technique, namely, random forest technique, with acceleration responses from structures. The ensemble-based approach provides a better prediction than an individual model. Random forest is a machine learning algorithm which has several decision trees to perform a task. The proposed approach develops a random forest as a regressor to predict multiple output variables, which is the vector of elemental level damage quantification results of the structure. Damage severity is identified as the reduction in the elemental stiffness parameters. The acceleration responses for single-element and multiple element damage cases are generated and further processed to feed as input to the random forest. The acceleration responses from the sensor nodes are concatenated, and principal component analysis (PCA) is applied to reduce the uncorrelated input dimension. The proposed approach can provide good damage identification results efficiently with much less computational demand and time, compared to the neural network training methods and deep learning models. Moreover, fewer sensors are needed to measure acceleration responses to localise damage and quantify the damage level. To demonstrate the proposed method, a simply supported beam is used as an example in numerical studies. Different levels of noise in the acceleration responses and uncertainty in the finite element modelling are considered. Experimental studies on a steel frame structure with 70 elements are also conducted to investigate the performance of the proposed approach for structural damage quantification. Good identification results are obtained with an efficient training process.

A numerical study on local characteristics of predetermined irregular wave trains

HURDNet: Heterogeneous UNet Structure With Range-Null Space Decomposition for Hyperspectral Image Reconstruction

Analysis of the Tooling Process for the Closed Oil Tank in Bushing

Through analyzing the mechanism of lubricating oil film on the rotating components,the new requirements of oil film structure was presented,on the basis of these requirements the structure of closed oil tank were presented,which difference from the traditional forms.The tooling process of the closed was designed through analyzing its processing,and then the full-closed oil tank was processed by the tooling process.Practice shows that these can improve the characteristics of lubricating in the rotary parts,reduce the wear and heat in the rotary parts,and improve transmission efficiency and simplify the way of lubrication.

Structural behavior and vibration characteristics of geopolymer composite lightweight sandwich panels for prefabricated buildings

An equivalent structural stress-based fatigue evaluation framework for rib-to-deck welded joints in orthotropic steel deck

Coincidences of the Concave Integral and the Pan-Integral

In this note, we discuss when the concave integral coincides with the pan- integral with respect to the standard arithmetic operations + and ·. The subadditivity of the underlying monotone measure is one sufficient condition for this equality. We show also another sufficient condition, which, in the case of finite spaces, is necessary, too.