Robotic Systems and Applications
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Study of a two-stage wood chip hydrolysis process and the kinetics of monosaccharide formation
Full text linkThis article studies the sulfuric acid hydrolysis of paulownia and poplar wood chips using a two-stage approach. The research analyzes the temperature-dependent kinetics of the hydrolysis process, nothing a peak yield of reducing agents at 170 °C. An increased likelihood of reducing agent decomposition is observed as temperature rises, especially at 180 °C. Using liquid chromatography, the amounts of glucose, fructose, and arabinose in the hydrolysates were measured, with the largest glucose yield obtained at 170 °C. In addition, High-performance liquid chromatography (HPLC) and Fourier-transform infrared (FTIR) spectroscopy showed the emergence of new functional groups in the hydrolyzed wood structure. Based on mathematical modeling and experimental validation, the two-stage hydrolysis method is regarded as a good technique. This method helps raise the of hydrolysates, which are employed as raw materials in biopolymer manufacture
Research on coupling dynamic characteristics and parameter influence of TBM cutterhead system
Full text linkAs an important system of TBM, the host system bears the impact of unstable load from itself and the strong load of the rock in the geological layer during operation, which causes irregular vibration of the host system, resulting in low tunneling efficiency, and is more likely to cause cutterhead cracking and component damage. To this end, with the help of analysis software such as Matlab and Ansys, the intrinsic characteristics and vibration response of the host system are studied, and the specific parameters of the vibration influencing factors are discussed. The results show that the axial displacement of the center block of the cutterhead is the largest, reaching 0.85 mm, and the longitudinal displacement value is about 2-3 times of the transverse displacement; in the design stage, the mass of the central block should be controlled in the range of 50 %-55 %, and the rest of the cutterhead should be controlled in the range of 12.5 %-13.5 %; the vibration is the smallest under the uniform layout of the gear, the fluctuation of the solid short shaft connection of the motor is relatively stable, and the maximum vibration value does not exceed 3.5e-2 mm
Modal and optimization analysis of a 12-degree-of-freedom engine mount system considering engine elasticity
Full text linkThe multi-degree-of-freedom engine mount system presents a coupling issue that significantly impacting its vibration isolation performance. Although the optimization theories for decoupling 6-degree-of-freedom (6-DOF) and 12-degree-of-freedom (12-DOF) engine mount systems are relatively well-developed, previous studies have predominantly focused on engine response and often overlook the impact of car body vibrations. To address this gap, this article conducts an in-depth investigation into how the elasticity of the car body affects the vibration isolation performance of the engine mount system. Initially, the dynamics of the engine mount system are modeled with 6 degrees of freedom, incorporating an elastic base with 9 and 12 degrees of freedom, respectively. The study then analyzes how body elasticity influences the natural frequencies and modal shapes of the engine mount system. Subsequently, the sensitivity of the engine mount system is assessed using Isight analysis to evaluate the three directional stiffnesses of the mount. Finally, the decoupling optimization of the 12-degree-of-freedom engine mount system is performed using the NLPQL (Sequential Quadratic Programming) method. The findings indicate that: (1) considering the car body’s influence directly affects the natural characteristics and decoupling efficiency of the engine mount system; (2) body elasticity in the Z-direction has the greatest impact on the system’s vertical natural frequency; and (3) the NLPQL method effectively enhances the decoupling rate of the engine mount system
Towards explainable artificial intelligence with potential games
Full text linkExplainable Artificial Intelligence (XAI) emerged when researchers attempted to identify methods that would interpret the models that are used to perform classification and predictions, in order to avoid having a black box just informing about the result. Methods of XAI are crucial to determine details of the model feature contribution towards the result. One of these methods is attributed to cooperative game theory and especially Shapley values. With this method the features are considered as players and the marginal contribution of the features are employed. In this paper, we take onboard the Potential Game paradigm to show the interconnection between them and the Shapley values. We show that the Shapley values are interlinked with the potential function. Moreover, we setup a game with the marginal contribution of the players as their utility functions and we prove that the game is a potential game. Finally, we show that the price of stability of this game is 1. We utilise the Simulated Annealing (SA) method to find the optimal solution
Small targets detection in low-resolution remote sensing images based on super-resolution joint optimization
Full text linkWhile convolutional neural networks have driven remarkable progress in remote sensing object detection, persistent challenges remain in detecting small targets within low-resolution imagery due to their limited pixel representation and feature degradation during hierarchical downsampling. To address this, this study proposed the joint super-resolution and detection network (JSRDN), which synergistically optimizes SR reconstruction through task-specific detection feedback, significantly enhancing small target recognition in LR remote sensing imagery. Firstly, generator in generative adversarial network incorporates improved residual blocks, enabling enhanced perception of complex deep-level features in the SR reconstruction process. Then, a perceptual loss function is introduced into the adversarial training process, which captures perceptual discrepancies in high-level features between reconstructed images and original HR references. After that, an edge-enhancement network is designed to dynamically detect edges in intermediate features restored by the generator, prioritizing edge influence across network layers to generate discriminative features for target recognition. Furthermore, the JSRDN implements detection-driven feedback by backpropagating object recognition loss through the generator, enforcing the super-resolution process to prioritize detection-salient feature recovery. Evaluated on 64×64 low-resolution COWC datasets, JSRDN achieves 0.1819 dB peak signal-to-noise ratio (PSNR) and 7.18 % average precision (AP) improvements over the deep residual dual-attention network (DRDAN), with ablation studies and visualizations confirming its balanced optimization of reconstruction fidelity and detection-oriented feature learning. This technology can provides valuable support for small target measurement and opens new opportunities in the field
A k-kNN miscalibrated current transformer identification method based on line topology for distribution networks
Full text linkThe operational duration and environmental factors associated with current transformers (CTs) in distribution networks makes them prone to measurement miscalibration during their operation. To address this, a kernel k-nearest neighbor (k-kNN) miscalibrated CT identification method based on line topology is proposed. This method relies on the composite characteristics of load currents specific to certain line topologies. High-precision secondary-side CT current data provided by the current acquisition devices in the feeder area are used to construct a multiple linear regression model. The multiple linear regression model is established in the complex domain, and indirectly assesses the measurement status of the current transformers by analyzing the complex coefficients. Building upon the kNN identification algorithm, a kernel function is introduced to map low-dimensional distance feature vectors into a higher-dimensional feature space where linear separability is significantly enhanced, thus improving the accuracy with which abnormal coefficients can be detected in the multiple linear regression model. Experimental simulations and field application scenarios demonstrate that the proposed method significantly outperforms traditional kNN algorithms in terms of classification performance. Specifically, there is an increase of 12.0 % in the F1 score, a rise of 13.3 % in accuracy, and an improvement of 12.0 % in recall. Moreover, in practical engineering applications, the recognition metrics consistently exceed 93 %, which substantiates the effectiveness of the proposed miscalibrated CT identification method
Bifurcation and chaos analysis of the floating raft vibration isolation system with quasi-zero-stiffness isolators
Full text linkThis paper presents an investigation into the nonlinear dynamic behaviors of the floating raft isolation system coupled with quasi-zero-stiffness isolators subject to multiple disturbance sources. First, the coupling effects between the excitation source and isolation system are considered. Also, the floating raft isolation model under multiple excitations and its motion equation are deduced, and then the dynamic responses are mainly investigated by using the techniques of time history diagram, spectrum diagram, phase diagram and Poincaré map, and the bifurcation diagram. Finally, the bifurcations of the mechanical isolation system with different parameters are analyzed through numerical methods, especially the effect of center distance and mass ratio. The result predicts that the floating raft shows an alternate phenomenon of periodic motion, quasi-periodic motion, and chaotic motion when the center distance and mass ratio vary. The motion state of the floating raft vibration isolation system is more sensitive to the mass ratio than to the center distance. The horizontal and rotational response of the system becomes very intense in the chaotic state, and the response amplitudes in the horizontal and vertical directions reach the same order of magnitude. Above all, the dynamic characteristics can provide the theoretic supporting for the dynamics, vibration control and its parametric optimization of the floating raft isolation system coupled with quasi-zero-stiffness isolators. This study will lay down the requirements for the engineering design and application of floating raft isolation equipment in large vessel
Simulation analysis of coupling mechanism between transient flow field characteristics of bubble collapse and metal deformation based on surface micromorphology
Full text linkIn the process of modifying titanium alloy oral implants using cavitation water jet, the collapse of bubbles releases significant energy. This phenomenon is accompanied by micro-jets and shock waves, which induce changes in the three-dimensional microscopic morphology of the implant surface. The loose and porous surface of the implant will increase the adhesion area of the cells, which is more conducive to the combination of the oral implant with the surrounding bone tissue. In order to explore the coupling mechanism between the instantaneous energy of bubble collapse and the surface deformation of titanium metal, based on different flow field and solid field model parameters, the numerical analysis software Ansys and the fluid-structure coupling simulation method are used to establish the numerical simulation model of single bubble collapse on the near curved wall. In order to explore the coupling mechanism between the instantaneous energy of bubble collapse and the surface deformation of titanium metal, the bubble growth process is ignored. Based on different flow field and solid field model parameters, this paper adopts the numerical analysis software Ansys and the fluid-structure coupling simulation method to establish the numerical simulation model of single bubble collapse on the near curved wall. The effects of flow field parameters and wall morphology on the transient flow field of bubble collapse and the effect of metal surface modification are revealed. The results show that when the initial bubble diameter is 180 μm, the instantaneous collapse high pressure reaches 7.24 GPa, and the maximum stress on the titanium surface is 689 MPa, which is 1.57 times higher than that under the bubble diameter of 60 μm. When the bubble collapses away from the wall, due to the weakened constraint of the wall, more intense energy is released, but the energy decays rapidly in the propagation process, and the energy loss when it reaches the wall is more serious. In this paper, the surface micromorphology is simplified into a near-curved shape. After the modification, the flow obstruction on the near-curved concave wall inhibits bubble collapse, resulting in an increase in bubble collapse time. The stress and deformation caused by a single bubble collapse are concentrated within a radius of 1mm and a depth of 5 μm
Enhancing loess deformation resistance using waste tire rubber particles
Full text linkLoess, characterized by its large pore structure and vertical joints, is prone to collapsible deformation upon moisture infiltration and significant settlement under load, threatening the stability of buildings and infrastructure. This study systematically investigates the effects of rubber particle size (10, 20, 40, and 100 mesh), content (0 %, 5 %, 10 %, 15 %, and 20 % by volume), moisture content, and freeze-thaw cycles on the deformation properties of loess. This systematic investigation distinguishes itself by using waste tire rubber particles as the sole amendment to elucidate both the individual and coupled effects of these factors. Results demonstrate that incorporating rubber particles significantly reduces the compression coefficient of loess, with optimal compressibility achieved at a 5 % rubber particle content and 40 mesh particle size. The collapsibility coefficient is minimized at a 20 mesh particle size with the same 5 % content. Moisture content significantly influences deformation behavior, with both high and low levels increasing the compression and collapsibility coefficients. The study also reveals that rubber particle-loess mixtures exhibit superior freeze-thaw resistance, with smaller increases in deformation coefficients after multiple freeze-thaw cycles compared to remolded loess. The particle size and content of rubber particles are identified as the most important factors influencing the compressibility and collapsibility of loess. This research provides specific guidelines for optimizing rubber particle size and content, controlling moisture levels, and evaluating freeze-thaw impacts to enhance the engineering performance of loess. The findings offer a scientific basis for sustainable waste tire management and advance the application of rubber particles in geotechnical engineering
Self-supervised CNN for user behavior analysis on smart meter data
Full text linkSmart 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