提高感应无线位置检测分辨率的方法

感应无线位置检测技术已经广泛应用于工业机车自动化,机车全自动无人操作的精确定位,要求进一步提高位置检测分辨率。文中以感应无线车上位置检测技术为研究对象,对应用中影响检测分辨率的原因进行了分析。提出了提高检测分辨率的方法:设计一种独特的感应线圈结构,采用具有抑制干扰能力副线圈检测精密位置,并且在编码电缆的交叉处采用同一信号参数计算一般位置与精密位置而消除内部串扰的影响。实验结果表明所述方法对提高检测分辨率有明显效果。     

C波段高稳定度磁控管微波源实验研究

由于物理机制和工艺结构的固有限制,各种微波源均有其功率极限,而以锁相技术为基础的功率合成技术是获得大功率微波的一条重要途径。主要研究了以磁控管为基本单元的注入式锁相方案,并且采用注入锁相方法对现有的c波段磁控管进行了锁相实验验证,研制出频率、相位、幅度可调的高稳定度磁控管微波源,并结合实验分析了系统的性能指标。     

Pd–La0.6Sr0.4Co0.2Fe0.8O3–δ composite as active and stable oxygen electrode for reversible solid oxide cells

To promote the electrocatalytic activity and stability of traditional La_0.6Sr_0.4Co_0.2Fe_0.8O_3–δ (LSCF) oxygen electrodes in reversible solid oxide cells (RSOCs), conventional physical mixed method was used to prepare the Pd-LSCF composite oxygen electrode. The cell with Pd-LSCF|GDC|YSZ|Ni-YSZ configuration shows perfect electrochemical performance in both solid oxide fuel cell (SOFC) mode and solid oxide electrolysis cell (SOEC) mode. In the SOFC mode, the cell achieves a power density of 1.73 W/cm² at 800 °C, higher than that of the LSCF oxygen electrode with 1.38 W/cm². In the SOEC mode, the current density at 1.5 V is 1.67 A/cm² at 800 °C under 50 vol% steam concentration. Moreover, the reversibility and stability of the RSOCs were tested during 192 h long-term reversible operation. The degradation rate of the cell is only 2.2%/100 h and 2.5%/100 h in the SOEC and the SOFC modes, respectively. These results confirm that compositing Pd with the LSCF oxygen electrode can considerably boost the electrochemical performance of LSCF electrode in RSOCs field.     

Application of machine learning algorithms for predicting the engine characteristics of a wheat germ oil–Hydrogen fuelled dual fuel engine

In this research work, performance and emission parameters of wheat germ oil (WGO) -hydrogen dual fuel was investigated experimentally and these parameters were predicted using different machine learning algorithms. Initially, hydrogen injection with 5%, 10% and 15% energy share were used as the dual fuel strategy with WGO. For WGO +15% hydrogen energy share the NO emission is 1089 ppm, which is nearly 33% higher than WGO at full load. As hydrogen has higher flame speed and calorific value and wider flammability limit which increases the combustion temperature. Thus, the reaction between nitrogen and oxygen increases thereby forming more NO. Smoke emission for WGO +15% hydrogen energy share is 66%, which is 15% lower compared to WGO, since the heat released in the pre-mixed phase of combustion is increased to a maximum with higher hydrogen energy share compared to WGO. Different applications including internal combustion engines have used machine learning approaches for predictions and classifications. In the second phase various machine learning techniques namely Decision Tree (DT), Random Forest (RF), Multiple Linear Regression (MLR), and Support Vector Machines (SVM)) were used to predict the emission characteristics of the engine operating in dual fuel mode. The machine learning models were trained and tested using the experimental data. The most effective model was identified using performance metrics like R-Squared (R²) value, Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE). The result shows that the prediction by MLR model was closest to the experimental results.     

Effect of the thermally grown oxide and interfacial roughness on stress distribution in environmental barrier coatings

To clarify the role of interface morphology and thermally grown oxide (TGO) in the failure of environmental barrier coatings (EBCs). In this study, the effect of chemical expansion on free energy was considered based on the continuous thermodynamic framework. The effects of roughness and TGO growth on the stress distribution of EBCs were investigated. The results showed that the stress coupling effect led to the inhomogeneous growth of TGO by affecting the gas diffusion and gas inflow rate. The TGO thickness at the peak increased with increasing roughness, and the TGO thickness at the valley and the middle position decreased with increasing roughness. The y-direction at the TGO/EBC valley and the TGO/BC peak under tensile stress increased with the TGO thickness and roughness and may be the first to fail in delamination. The calculation results of the model can provide a theoretical basis for the coating design and manufacturing process.     

Sr and Fe co-doped Ba2In2O5 as a new proton-conductor-derived cathode for proton-conducting solid oxide fuel cells

BaSrInFeO₅ (BSIF), a new cathode material for proton-conducting solid oxide fuel cells (SOFCs), is designed based on the modification of the Ba₂In₂O₅ proton conductor with Sr and Fe cations. Compared with the Ba₂In₂O₅ proton conductor tailored with only Fe cations (Ba₂InFeO₅, BIF), doping Sr can improve the chemical stability and also benefit the formation of oxygen vacancies. The proton mobility is also improved with Sr-doping, which is confirmed by first-principles calculations and experimental studies. An H-SOFC using the BSIF cathode generates a relatively high peak power density of 1192 mW cm^-2 at 700 ^oC, which is superior to many cells in previous reports. First-principles calculations find that the cathode oxygen reduction reaction (ORR) energy barrier for BSIF is significantly lower than that for BIF. Although Ba₂In₂O₅ is less studied, the derived cathode materials can still present decent performance, probably offering new material selections for H-SOFCs.     

Research on the acceptance of collaborative robots for the industry 5.0 era -- The mediating effect of perceived competence and the moderating effect of robot use self-efficacy

Collaborative robots (Cobots), an important component of the Industry 5.0 era, have been rapidly entering a variety of industrial application scenarios. However, employees working with them are reluctant to accept cobots into the workplace. Therefore, the traditional technology acceptance model (TAM) is unsuitable for research on the acceptance of cobots with artificial intelligence and the human-robot interaction process. In addition, anthropomorphism cannot explain the lower employee acceptance with the increase of cobots anthropomorphic from the mechanistic level. Therefore, based on the human-robot interaction phenomenon in the emerging industrial field, combined with the Uncanny Valley effect and intergroup threat theory, 300 subjects were invited to conduct an empirical study using experimental vignette methodology (EVM). The findings are as follows: 1) Perceived competence plays a mediating role in the relationship between cobots anthropomorphic and acceptance of cobots; 2) Perceived competence and perceived threat serially mediates the relationship between cobots anthropomorphic and acceptance of cobots; 3) The cobot use self-efficacy plays a moderating role in the relationship between perceived competence and perceived threat. The research results provide a mechanistic explanation for alleviating the low acceptance of cobots, give measures and methods to improve acceptance of cobots and provide solutions for the promotion and application of cobots in the industrial field.     

Assessment of visual fatigue caused by stereoscopic disparity based on multimodal measurement

Given the prevalence of visual fatigue induced by stereoscopic display, research on the neural mechanism of visual fatigue was limited. The goal of this study was to investigate the effect of stereoscopic display with different horizontal disparities on visual fatigue based on subjective questionnaire, ophthalmological parameters, eye movement and electrocardiogram (ECG). Subjects felt more headache, uncomfortable and dry eyes after watching 70-minute enhanced 3D display with larger negative disparity than normal 3D display. Meanwhile, visual function tear film break-up time (TBUT) reduced, microsaccade mean pupil diameter (MMPD) increased and heart rate variability (HRV) changed more significantly after watching enhanced 3D display. Overall, these results demonstrated that enhanced 3D display had a better sense of immersion, but it induced more visual fatigue significantly than normal 3D display.     

A New Hybrid Model for Segmentation of the Skin Lesion Based on Residual Attention U-Net

Skin segmentation participates significantly in various biomedical applications, such as skin cancer identification and skin lesion detection. This paper presents a novel framework for segmenting the skin. The framework contains two main stages: The first stage is for removing different types of noises from the dermoscopic images, such as hair, speckle, and impulse noise, and the second stage is for segmentation of the dermoscopic images using an attention residual U-shaped Network (U-Net). The framework uses variational Autoencoders (VAEs) for removing the hair noises, the Generative Adversarial Denoising Network (DGAN-Net), the Denoising U-shaped U-Net (D-U-NET), and Batch Renormalization U-Net (Br-U-NET) for removing the speckle noise, and the Laplacian Vector Median Filter (MLVMF) for removing the impulse noise. In the second main stage, the residual attention u-net was used for segmentation. The framework achieves (35.11, 31.26, 27.01, and 26.16), (36.34, 33.23, 31.32, and 28.65), and (36.33, 32.21, 28.54, and 27.11) for removing hair, speckle, and impulse noise, respectively, based on Peak Signal Noise Ratio (PSNR) at the level of (0.1, 0.25, 0.5, and 0.75) of noise. The framework also achieves an accuracy of nearly 94.26 in the dice score in the process of segmentation before removing noise and 95.22 after removing different types of noise. The experiments have shown the efficiency of the used model in removing noise according to the structural similarity index measure (SSIM) and PSNR and in the segmentation process as well.     

Thermochemical conversion of waste printed circuit boards: Thermal behavior,reaction kinetics,pollutant evolution and corresponding controlling strategies

With the rapid development of the global electronics industry, waste printed circuit boards (WPCBs) has become one of the world's fastest growing waste streams. Exploring an environmentally sound treatment for this abundant and multi-component waste is critical to its sustainable development. This study has been aimed to cover thermochemical conversion of WPCBs (combustion, pyrolysis, gasification and hydrothermal process), focusing on thermal behavior, reaction kinetics, pollutant evolution and corresponding controlling strategies, with the aim of promoting circular economic development and building a sustainable future for the electronics industry.