In mobile robot path planning, the conventional A* algorithm often suffers from redundant node expansion and excessive turning points, whereas the Dynamic Window Approach (DWA) is prone to local optima and deviations from the global path in dynamic environments. To address these issues, this paper proposes a hybrid algorithm, termed A*-GA-DWA, which combines an improved A* algorithm with a GA-optimized DWA method. In the global planning stage, a directional six-neighborhood search strategy, an obstacle-aware adaptive heuristic function, and a turning-point smoothing method are introduced to improve path quality and reduce redundant node expansion. In the local planning stage, genetic algorithm optimization is applied to the DWA evaluation weights to enhance obstacle avoidance adaptability in dynamic environments. In addition, key nodes extracted from the global path are used as sub-goals to strengthen the coordination between global guidance and local replanning. Simulation results on a 30 & times; 30 map with dynamic obstacles show that, compared with conventional A*-DWA, the proposed method reduces the path length by 14.07% and the navigation execution time by 45.98%; compared with M-A*-DWA, the path length and navigation execution time are further reduced by 0.32% and 21.23%, respectively. Additional experiments on a ROS-based mobile robot platform were conducted to further validate the deployability and obstacle-avoidance capability of the proposed framework. These results provide an effective solution for mobile robot path planning tasks.

Predicting core loss under high-frequency non-sinusoidal excitation is crucial for power electronics equipment design. Temperature significantly affects core loss, and traditional core loss prediction models typically incorporate temperature corrections to enable accurate loss estimation across varying temperatures. Based on the Modified Steinmetz Equation (nonT-MSE) model, this study considers the temperature effect by employing a combination of the Tanh function and a linear term to modify the three empirical parameters, with the Tanh function capturing the nonlinear saturation of the loss coefficient k with increasing temperature. This leads to the establishment of the temperature-corrected non-TMSE (T-MSE) model for predicting magnetic core loss under high-frequency non-sinusoidal excitation. During model derivation, training data undergo logarithmic transformation processing. Subsequently, with T-MSE empirical parameters as variables and the minimum mean squared error between T-MSE predicted values and experimental values as the objective function, a single-objective optimization model is established. Finally, the empirical parameters of T-MSE are calculated using the training data and the single-objective optimization model. Comparing the core loss experimental results of the four materials, the average MSE values for the T-MSE model, the nonT-MSE model, and the square-root temperature-corrected non-TMSE model proposed by Zeng et al. (Zeng) are 0.0082, 0.0459, and 0.0110, respectively; with average MAPE of 1.57%, 1.87%, and 2.17%, respectively; and average R2 of 0.9862, 0.9807, and 0.9731. Compared to the nonT-MSE model and the Zeng model, the T-MSE model demonstrated higher prediction accuracy.

Thermoelastic damping (TED) has been identified as the primary mechanism of energy dissipation for micro-resonators operated in vacuum. Consequently, the accurate estimation of quality factor (Q) according to TED (QTED) is critical for the optimal design of micro-resonators. For rectangular cross-section micro-ring resonators in out-of-plane vibration, this work firstly introduces the nonlocal-dual-phase-lagging (NDPL) and modified-couple-stress (MCS) theories to derive the comprehensive and analytical QTED formulae. Initially, the essential coefficients in the QTED models are verified by the ratios of normal-strain energy to stored energy in the micro-resonators and then discussed from an energy-ratio perspective. Subsequently, TED results of the classical existing and current models are examined incorporating the finite-element results. Finally, main explorations focus on the multiple-physical effects dependence of the fluctuant temperature, TED spectra, and the equivalent thermal-relaxation times. The results reveal that the MCS size-dependent effect can inhibit TED and improve QTED. The NDPL effect significantly contributes to the fluctuant temperature and TED spectra at high frequencies. Additionally, when the vertical thickness of micro-rings is large enough, the equivalent thermal-relaxation times corresponding to TED peaks will approach defined limit values.

Flight safety, as the core of civil aviation transportation industry, has attracted much attention from society in recent years. Based on the concept of aviation safety and data-driven, this article innovatively proposes a direct multistep prediction model and early warning strategy based on QAR real-time flight parameter data. First, based on the data preprocessing, COG NORM ACCEL parameter was used as the prediction index, random forest algorithm was used to analyze the importance of the remaining 29 flight parameters, and 6 important flight parameters were selected for real-time prediction. Then, based on a dynamic weight allocation mechanism, an end-to-end two-layer fusion prediction model was constructed. The model employed LSTM, transformer, and TCN as parallel base models, where complementarity among multiple models was achieved through feature-level first-layer fusion and prediction-level second-layer fusion. Numerical experimental results demonstrated that the proposed fusion strategy not only achieved hierarchical improvement in prediction performance but also enhanced the interpretability of prediction results. Finally, an early warning strategy based on the real value and sliding window volatility was established, and its effectiveness is verified by the simulation of CAP CLM 1 POSN. In conclusion, this article provides a new data-driven approach for aviation safety management, which helps to identify and deal with potential flight risks in advance, and provides a strong reference for the automated early warning system of airlines.

Aiming at the problems of slow convergence speed and easy to fall into local optimal solutions in the application of ant colony algorithm for path planning, a path planning fusion algorithm based on improved A* and ant colony is proposed. Firstly, the A* algorithm is improved by introducing a dynamic weighting factor Q to improve the search speed of the algorithm; then, the improved A* algorithm is utilized for path preplanning, and the regional pheromone concentration of the ant colony algorithm is redistributed to reduce the convergence time of the ant colony and to improve the path search efficiency; secondly, a new weight function is added and the transfer probability of the ant colony is improved to reduce the blindness of the transfer and the number of iterations; and finally. Three times B-splines are introduced to optimize the smoothness of the path. Analyzed by MATLAB simulation in the map set in the paper, the fusion algorithm reduces the length of the optimal path by 5.96%, the number of iterations by 34.42%, and the average time consumed by 4.47% compared with the traditional ant colony algorithm.

Autonomous valet parking technology refers to a vehicle’s use of onboard sensors and line-controlled chassis to carry out a fully automatic valet parking function, which can greatly improve the driver’s experience. This study focuses on autonomous parking, employing environmental modeling and vehicle kinematics models. Innovatively applying the PSBi-RRT algorithm to path planning in autonomous parking systems constitutes this research’s contribution to this field. Firstly, the environment is modelled by the raster method; then, the PSBi-RRT algorithm is used for path planning, and a B-spline curve is used for path optimization. Speed and acceleration are smoothed at the same time, and finally, a smooth and obstacle-avoiding path planning scheme is obtained. The results show that an autonomous parking system based on the PSBi-RRT algorithm performs path planning from the vehicle to the parking space. Compared to RRT and Bi-RRT, PSBi-RRT generates shorter planning paths, smoother heading angle changes, shorter planning times, fewer nodes, and higher success rates. This research provides theoretical support for the development of autonomous parking technology.

The utilization of tempered blast-furnace slag through the direct fiber forming process to produce high-value thermal insulation materials offers a dual benefit: it efficiently utilizes the latent heat in the unused slag and significantly increases the value of blast-furnace slag utilization. However, measuring the melting properties of iron slag at high temperatures is challenging. In this study, the melting behavior of SiO2 in a high-temperature molten pool was investigated. We employ dynamic visual data (video stream) captured via a non-contact charge coupled device video recording system to extract SiO2 contours through image processing. The change in image centroid characteristics is used to establish a convolution function relationship, and MATLAB's traversal search algorithm determines the centroid position of SiO2. Given that SiO2 is proportionate to crucible pixels, the area of the SiO2 is calculated through pixel statistics within these contours. A new indirect method is then proposed to process image information to obtain SiO2 volume and mass at different time points. An exponential fitting yields the melting rate function of SiO2. Finally, this indirect method has been compared with shape from shading, quantitative characterization, and dimensional analysis techniques. Besides, the strengths and limitations of each method have been discussed. Our findings reveal that the indirect solution method presented here boasts straightforward calculation steps and imposes minimal image format requirements, which provides theoretical and technical support for the direct fiber forming process of blast-furnace slag.

With the booming development of door-to-door housekeeping service, the platform faces the problem of order assignment. Improving the matching mechanism between orders and housekeepers based on a dynamic programming (DP) algorithm can not only achieve flexible order allocation but can also improve the service efficiency and service quality. In this paper, a single objective nonlinear programming model is established, which takes the maximum total weight value as the objective function to study the order allocation problem under offline and online conditions. Under the offline condition, the number of housekeepers is taken as the decision variable. The status of order and housekeeper, order time, and action trajectory are taken as constraints. For online assignment, the order backlog status is treated as the decision variable. The reliability of the model was verified using real data from 20 groups of housekeepers and 50 groups of orders. Finally, the effect of order backlog on online allocation is discussed and the optimal threshold and maximum weight are found. The online order assignment model is compared with the nearest distance assignment model. The results show that the online assignment model with a total weighted score of 1045.14 is better than the nearest distance assignment model with a score of 810.25.

Abstract The squeeze‐film damping (SQFD) is an important dissipation mechanism of Micro‐Electro‐Mechanical Systems resonators. The current SQFD models of perforated plates treat borders of plate and holes as the constant pressure boundary, without considering border effect. In this paper, the border effect on SQFD is studied by expanding simulation area. At the same time, based on the research of non‐perforated plate border effect, the calculation size of the perforated plate hole cell is modified, and the modified SQFD model of the perforated plate has been built. Compared with simulation results, it shows the border effect has a great influence on SQFD of perforated plate. The precision of the modified model is higher than that of recent models. For a rectangular plate, the maximum error of the modified model is 12%, while for the recent model it is 40%. For a circular plate, the modified model is 38%, while the recent model is 58%.

Breast cancer is the most common malignancy in women worldwide. The pathogenesis of this disease is closely related to the estrogen receptor alpha subtype (ERα). Therefore, it is of great importance to develop effective inhibitors of ERα activity for the treatment of breast cancer. In this paper, we propose a novel ensemble machine learning model for quantitative structure-activity relationship of anti-breast cancer drugs, which can effectively predict drug activity in small samples with multiple characteristic variables. To avoid the problem of over-fitting caused by low-correlation independent variables, the scoring mechanism of random forest was improved by incorporating three relevance indicators, including the maximum mutual information number, Pearson correlation coefficient and distance correlation coefficient, and 20 optimal molecular descriptors were selected. The Bayesian hyperparameter optimization method was used to optimize the parameters of multiple linear regression (MLR), support vector regression (SVR), and extreme gradient boosting (XGBoost), respectively. The AdaBoost strong learner was constructed by combining the weak learner with the weighted linear addition method. The results show that the proposed ensemble learning model has the best prediction performance compared to the three basic learner models and the CNN-LSTM combination prediction model. The root mean square error was reduced by 7.60%-26.51%. The mean relative error was reduced by 6.46%-30.92%. Goodness of fit increased by 9.57%-36.94%. Finally, the biological activities of 50 candidate compounds for ERα inhibitors were predicted, and it was found that 4-[2-benzyl-1-[4-(2-pyrrolidin-1-ylethoxy)phenyl]but-1-enyl]phenol had an excellent biological activity value pIC50, which had the potential to be an ERα inhibitor. The model proposed in this paper has good prediction accuracy, which can provide an effective reference for the discovery and development of anti-breast cancer drugs.

电子器件的薄板常采用胶膜进行组装,为了保证器件的正常工作,胶装过程中薄板与基底的对中要求很高,甚至达到微米级别,单纯利用销孔机械定位难以达到装配精度.胶装过程中,胶膜在烘箱中熔化,胶水渗透到薄板定位孔与基底定位销,利用销孔间隙中形成的液面表面张力,自动调整薄板的位置进行组装.本文利用仿真模型,具体分析销孔壁面的亲疏水性对自组装的影响,结果表明,销孔中一个是亲水性、另一个是疏水性,有利于装配的自组装,而销孔都是亲水或疏水,表面张力会导致销孔定位偏差.

The steering knuckle is a crucial component of student racing vehicles, designed by the Formula Society of Automotive Engineers (FSAE). Developing a lightweight (LW) vehicle that meets the requirements of the student formula car presents a challenge. This study presents a LW design of the steering knuckle using the Topology Optimization (TO) approach for the Formula Society of Automotive Engineers (FSAE) competition considering two different mass constraints (40% and 48%). Moreover, the research includes Stress–Life (SN) curves for three materials, structural steel, 4130 steel, and AISI 1020 steel, providing essential insights into the fatigue characteristics of the model. The results compare the three materials and two mass reduction levels, with steel 4130 achieving a significant mass reduction of 42.70%. Additionally, steel 4130 exhibits superior performance in weight reduction, stress, deformation, and safety aspects. The optimized design meets the criteria for strength, stiffness, and safety under various conditions. The fatigue analysis reveals that AISI 1020 and steel 4130 have superior endurance (1 × 105 and 2 × 105 cycles, respectively). This research provides significant contributions to the development of a LW, high-performance steering knuckle for student formula racing vehicles, highlighting the significance of TO and material selection in achieving optimal outcomes.

池沸腾作为一种高效的冷却方式,在电动汽车电池热管理系统(TMS)的应用中受到关注.传热系数(HTC)是池沸腾重要的热力学限制参数之一,直接影响池沸腾的传热性能.对影响池沸腾HTC的主要因素,如表面结构、饱和压力和表面湿润度等进行介绍,归纳提高池沸腾HTC的方法.综述将池沸腾冷却技术应用于电池模组TMS的研究进展,与传统的风冷、液冷和热管散热方式相比,池沸腾冷却效率大幅提高.对未来电动汽车电池模组的TMS研究方向进行展望,为池沸腾研究中HTC相关实验、池沸腾冷却技术应用于电池模组的冷却提供技术参考.

在国内新能源汽车行业迅速发展的背景下,国内本科院校对车辆专业课程进行了相应的改革,但其中专业课程改革存在简单缩减传统课程、增加新课程现象,使得传统专业课程知识体系不连贯,新专业课程与先导课程无法建立较强知识联系.这种情况容易导致学生课堂学习困难、自主学习性降低、培养质量下降、就业困难等问题.文章针对车辆专业课程改革展开研究,以传统专业课汽车发动机为例,从学以致用、循序渐进的学习角度出发,在传统专业课程到新能源汽车课程过渡时期提出课程改革的几点意见,保障学生知识体系健全,深化学生对专业理论知识的理解和应用,缓解就业压力.

近年来,自动驾驶技术作为智能网联汽车的重要组成部分,成为热点话题.环境感知系统是自动驾驶汽车的感官神经,扮演着"眼睛"的角色.文章通过查阅相关文献,结合当前研究热点,对自动驾驶汽车系统组成做简要介绍,同时对其环境感知系统所使用传感器的种类、工作原理、应用场景等方面做了系统介绍,对不同传感器进行对比分析并提出建议.通过介绍,能够帮助读者对自动驾驶汽车环境感知系统有初步了解,同时对相关技术人员给予一定指导.

化学品船在运输结束后,需要对船舱进行清理,目前采用的是人工抽洗和强制通风相结合的方式,工作效率较低且对人员的人身安全有一定的隐患.如何采用机械设备替代人工进入船舱作业是企业的迫切需求.但是由于较小的舱口尺寸和较高的防爆等级,市面上清洁机器人都较难满足企业要求.为此,本文设计一款小型化学品船舱清洁机器人,底盘采用轮式驱动,整体采用隔爆型设计,满足IIC类电气设备防爆要求,整体尺寸较小,空载质量小于30kg,机器人采用远程遥控操作,有实时摄像功能,具有清理地面残余液体的吸盘机构和抹布机构,可以清理残余液体10L.该机器人的设计可以满足化学品船舱的清理需求.

Breast cancer is the most common malignancy in women worldwide. The pathogenesis of the disease is closely related to the estrogen receptor alpha subtype (ERa). This paper presents a new optimization modeling method for breast cancer drug candidates. First of all, the research of random forest scoring mechanism was improved, using the maximum mutual information number - the Pearson correlation coefficient - distance correlation coefficient of three kinds of relevant indicators, select 20 each optimal molecular descriptors. Then, the Bayesian hyperparameter optimization method is used to optimize the parameters of extreme gradient boosting (XGBoost). The research results show that the model proposed in this paper has the best prediction performance compared with classical prediction models such as unoptimized XGBoost and SVR, and the goodness of fit is improved by at least 26.67%.

With the continuous development of electric vehicle (EV) technology, there is an increasing need to analyze the factors influencing customers’ purchase intentions. According to the data of customers’ vehicle experience evaluation and personal information, this paper develops the analysis models of influencing factors using the analysis of variance algorithm (ANOVA) and Kruskal–Wallis algorithm. Then, the purchase intention model for EVs is proposed using the random forest method. Finally, the optimization model for the EV sales plan was built. The results show that the main factors influencing customers’ purchases are different for different vehicle brands. However, the customer’s evaluation of the vehicle experience has a greater influence on the customer’s purchase. Compared to other prediction models, the random forest model has the highest accuracy. For 3 EV brands, the prediction accuracies are 97.8%, 98.9%, and 97.6%. In addition, this paper predicts the purchase intentions of 15 customers. By optimizing the sales plans for 3 EV brands, the predicted purchase rate of 15 customers increased from 40% to 53%. The research work contributes to the sales of electric vehicles, the accurate positioning of customers, and the identification of more potential customers.

Squeeze film damping is an important damping mechanism in MEMS resonators. Considering the end effect of MEMS squeeze film structure, Veijola modified the squeeze film damping model of Reynolds equation, and proposed a modified model for rectangular plates, but Veijola did not give a modified model of squeeze film damping of circular plate. In this paper, based on Vejiola's theory, the calculated size is corrected for the squeeze film of the circular plate, and establish a circular plate squeeze film damping modified model, which is verified by numerical simulation. The research results show that the damping error between the modified model and the numerical model is very small, with a mean error less than 1 %, while the unmodified model has an error of more than 7%. This paper also discusses the influence of the size of the circular plate radius and gap height on the end effect. It is found that the larger the radius of the circular plates and the smaller the height of the gap, the smaller the influence of the end effect on the squeeze film damping.

Lane line detection is one of the important tasks of the environment perception system of autonomous vehicles, which must be very time sensitive and robust. To this end, this paper proposes a lane line detection implementation method based on the OpenCV platform, which can be applied to smart cars in specific places, mainly including image preprocessing and lane line detection and fitting. Applying morphological operations to the image preprocessing stage can effectively fill in the wear information of lane lines, and the least squares method is used to adjust lane lines after Hough transformation. The results show that the proposed method can improve the operation speed without affecting the accuracy of the algorithm, and has certain practicality.