橡胶轮胎和软管用高强度钢帘线盘条的研发

为了减轻汽车重量,提高燃油消耗,研制了高强度钢帘线用线材。然而,开发高强度钢帘线的难点是必须同时获得高强度和良好的塑性。为了解决这一问题,通过分析拉丝过程中钢丝组织和力学性能的变化,研究了拉丝过程中的分层机理,提高了拉丝的塑性。采用1.0%C过共析钢和优化的拉拔工艺,在实验室可获得4400 MPa级钢帘线。     

Structural Aspects and Prediction of Calmodulin-Binding Proteins

Calmodulin (CaM) is an important intracellular protein that binds Ca²⁺ and functions as a critical second messenger involved in numerous biological activities through extensive interactions with proteins and peptides. CaM’s ability to adapt to binding targets with different structures is related to the flexible central helix separating the N- and C-terminal lobes, which allows for conformational changes between extended and collapsed forms of the protein. CaM-binding targets are most often identified using prediction algorithms that utilize sequence and structural data to predict regions of peptides and proteins that can interact with CaM. In this review, we provide an overview of different CaM-binding proteins, the motifs through which they interact with CaM, and shared properties that make them good binding partners for CaM. Additionally, we discuss the historical and current methods for predicting CaM binding, and the similarities and differences between these methods and their relative success at prediction. As new CaM-binding proteins are identified and classified, we will gain a broader understanding of the biological processes regulated through changes in Ca²⁺ concentration through interactions with CaM.     

特种工程胎硫化外胎质量缺陷原因分析以及解决措施

我公司使用的硫化机采用氮气定型,二次水硫化的工艺。由于特种工程胎胎体偏厚且大,成型机都比较大,生产过程中的帘布筒、钢丝、胎侧等部件都比较重且大。成型胎面使用胎面缠绕机缠绕,所以整个生产过程难度较大。特种工程胎硫化外胎的主要质量问题有:胎面皮泡、胎侧缺胶、胎侧泡、胎肩侧皮泡、子口内侧露线、子口缺胶、胎肚内缺(窝气)、胎肚露线、胎肚皮泡、胎肚串泡、子口支边、胎冠支边、子口鼓包、外胎花缺等。通过对特种工程胎硫化外胎质量缺陷原因分析,找到相应的解决措施,从而减少硫化外胎质量缺陷。     

Process knowledge-based random forest regression for model predictive control on a nonlinear production process with multiple working conditions

In process industry, predictive control approaches have been widely used for nonlinear production processes. Practically, the predictor in a predictive controller is extremely important since it provides future states for the optimization problem of controllers. The conventional predictive controller with precise mathematical predictors approximating the state space of physical systems is difficult and time-consuming for nonlinear production processes, and it performs poorly over a wide range of working conditions and with significant disturbances. To address the challenges, the trend of applying artificial intelligence emerges. However, the industrial process-specific knowledge is ignored in most cases. In this study, a predictive controller with a control process knowledge-based random forest (RF) model is proposed. Specifically, working data are clustered at first to handle diverse working conditions. Then, a process knowledge-based forest predictor, namely MIW-RF model with a redesigned cascading RF structure, is proposed to incorporate control process knowledge into modeling. Thus, future states of controlled variables could be more accurately acquired for the optimizer. A simplified version of the predictive model is also developed with quick model training and updating. The proposed predictive methods are finally introduced into the controller design. According to the empirical results, the proposed methods deliver a better control performance against benchmarks, including more accurate anticipated controlled-variable responses, better set-point tracking and disturbance rejection capability.     

基于机器视觉的轮胎表面缺陷检测系统的研究与应用

在传统的轮胎表面缺陷依靠人工检测,存在劳动强度高、受人的主观影响大以及效率低下的问题。针对这一现象,研究了一种基于机器视觉的轮胎表面缺陷3D检测系统。该系统依靠机器视觉系统获取检测轮胎的表面图像,然后创建3D模型、判定缺陷类型,最终实现实时自动预警,为轮胎生产商提供一种自动化检测方案。系统集成了先进的技术、软件和工具,配套的信息管控系统可以对轮胎型号和生产数据进行采集、存储、分析,以便在生产过程中实现更高效、更可靠的质量控制,具有较高的实际应用推广价值。     

巨型工程机械子午线轮胎高强度、低生热胎面基部胶配方开发

通过对国外知名品牌37.00R57和40.00R57规格巨型工程机械子午线轮胎胎面基部胶组成分析和物理性能测试,对我公司巨型工程机械子午线轮胎胎面基部胶配方进行优化设计。新开发配方半成品和成品胶料动态损耗因子分别降低37.9%和26.6%,胶料物理性能达到竞品水平,成品轮胎TKPH提升13.6%。     

A new method of predicting the saturation pressure of oil reservoir and its application

Saturation pressure is a vital parameter of oil reservoir which can reflect the oilfield characteristics and determine the oilfield development process, and it is determined by experiments in the laboratory in general. However, there was only one well with saturation pressure test in this target reservoir, and it is necessary to determine whether this parameter is right or not.In this work, we present a new method for quickly determining saturation pressure using machine learning algorithms, including random forest regressor (RF), support vector machine (SVM), decision trees (DT), and artificial neural network (ANN or NN). Using these approaches, saturation pressure was obtained by using the initial solution gas-oil ratio (GOR), temperature, API gravity and other reservoir-fluid data available in the oilfields. Compared with the empirical formula for saturation pressure calculation, the calculated result shows that the accuracy given from machine learning is higher than that from other formulas at home and abroad, and has a good match with the lab test. On the basis of the calculated saturation pressure, it can determine whether the reservoir enters into the stage of dissolved gas drive or not, which also provides the basis for maintaining the reservoir pressure by water injection in advance, rational development decision-making and work over measures.This approach above can provide technical guidance for predicting the saturation pressure in the development of different kinds of reservoirs, including the sandstone reservoirs and carbonate reservoirs.     

抗硫化返原剂PK900在全地形轮胎胎面胶中的应用

研究抗硫化返原剂PK900在全地形轮胎胎面胶中的应用。结果表明:在全地形轮胎胎面胶中添加抗硫化返原剂PK900,胶料硫化速度变化不大,抗硫化返原性能提高,在过硫条件下硫化胶可保持较好的物理性能;成品轮胎的耐久性能提高,使用寿命延长。     

基于LSTM-RF模型的城市道路交通流预测

交通流预测作为信号协调和出行时间预测等任务的基础,成为了交通领域的研究点。对于交通流预测问题,研究人员提出了多种方法,但这些方法大多只使用交通流数据的时域信息进行交通流预测,忽略了空间相关性对于预测目标路段流的影响,导致预测精度不理想。基于组合模型的思想提出了一种称为LSTM-RF的交通流预测模型。在交通流预测过程中,首先使用LSTM模型提取预测目标路段的时序特征,再将其预测值与采集的相邻上下游路段信息同时作为随机森林模型的输入特征,进行交通流时空相关性分析,获得最终的预测结果。并通过贵阳市车牌识别系统采集的城区132条路段的交通流数据进行实验验证。结果表明:该方法在预测精度上优于单一模型,并且预测误差相比单一模型有明显减少。     

Forest parameters retrieval with dual-baseline polarimetric SAR interferometry based on clustering analysis

Forest parameters inversion is an important application of Polarimetric Synthetic Aperture Radar Interferometry(PolInSAR). To resolve the volume decorrelation coefficient ambiguity of single-baseline PolInSAR, a new dual-baseline PolInSAR approach is proposed in this paper. The correlation of dual-baseline data is fully investigated to resolve the ambiguity problem effectively, and the performance of the proposed approach is proved to be of great robustness in a very small baseline ratio system. Finally, experimental results with ESA dual-baseline PolInSAR simulated data validate the effectiveness and robustness of the proposed approach.