EBZ260H型掘进机在隧道施工中的应用

以某工程为具体实例,介绍了EBZ2260H掘进机的适用范围、技术参数和主要配置;从掘进机的截割方法、维护和保养、除尘装置的正确使用等多个方面,对EBZ260H掘进机暗挖工艺和技术进行了详细阐述;归纳总结了EBZ260H掘进机的各项优点.     

技术防范电信欠费

电信用户欠费是当今电信业者面临的日益严峻的问题,本文主要阐述如何利用电信企业拥有的数据仓库,依靠技术手段分析追踪和预防用户异常行为,达到防范电信欠费的效果。     

电磁搅拌技术在莱钢六号合金钢连铸机上的设计与应用

介绍结晶器电磁搅拌在矩形坯连铸机上的应用。采用内置式结晶器电磁搅拌后,连铸坯低倍组织大为改善,质量明显提高。     

Single machine scheduling with simple linear deterioration to minimize total absolute deviation of completion times

We consider a single machine scheduling problem with simple linear deterioration. Job processing times are assumed to be a simple linear function of a job-dependent growth rate and the job's starting time. We seek an optimal schedule, so as to minimize the total absolute deviation of completion times (TADC). We prove several interesting properties of an optimal schedule, and introduce two efficient heuristics which are tested against a lower bound.     

Evaluation of tile–wall bonding integrity based on impact acoustics and support vector machine

It is well recognized that the impact-acoustic emissions contain information that can indicate the presence of the adhesive defects in the bonding structures. In our previous papers, artificial neural network (ANN) was adopted to assess the bonding integrity of the tile–walls with the feature extracted from the power spectral density (PSD) of the impact-acoustic signals acting as the input of classifier. However, in addition to the inconvenience posed by the general drawbacks such as long training time and large number of training samples needed, the performance of the classic ANN classifier is deteriorated by the similar spectral characteristics between different bonding status caused by abnormal impacts. In this paper our previous works was developed by the employment of the least-squares support vector machine (LS-SVM) classifier instead of the ANN to derive a bonding integrity recognition approach with better reliability and enhanced immunity to surface roughness. With the help of the specially designed artificial sample slabs, experiments results obtained with the proposed method are provided and compared with that using the ANN classifier, demonstrating the effectiveness of the present strategy.     

Surface Control and Electrical Tuning of MXene Electrode for Flexible Self-Powered Human–Machine Interaction

MXene materials emerge as promising candidates for energy harvesting and storage application. In this study, the effect of the surface chemistry on the work function of MXenes, which determines the performance of MXene-based triboelectric nanogenerator (TENG), is elucidated. First-principles calculations reveal that the surface functional group greatly influences MXene work function:  OH termination reduces the work function with respect to that of bare surface, while  F and  Cl increase it. Then, work functions are experimentally determined by Kelvin probe force microscopy. The MXene prepared by gentle etching at 40 °C for 48 h (GE_40/48) has the largest work function. Furthermore, an electron-cloud potential-well model is established to explain the mechanism of electron emission-dominated charge transfer and assemble a triboelectric device to verify experimentally its conclusions. It is found that GE_40/48 has the best performance with a 281 V open-circuit voltage, 9.7 µA short-current current, and storing 1.019 µC of charge, which is consistent with the model. Last, a patterned TENG is demonstrated for self-powered human–machine interaction application. This finding enhances the understanding of the inherent mechanism between the surface structure and the output performance of MXene-based TENG, which can be applied to other TENG based on 2D materials.     

数控机床操控器人机界面优化设计研究

目的控制器和显示器共同组成数控机床控制面板的整体界面,是机床接受与执行命令的交互平台。从人机交互角度,聚焦于控制器的界面布局进行优化设计。方法基于人机工程学理论的优化流程,采用现场访谈、现场观察等方法,将人机工程学的理论转化为具体考核指标。结论从人机工程学的研究角度出发,结合工业设计、用户体验设计学等内容,从功能区域分区、操纵器色彩设计等方面优化数控机床控制器的界面,提高系统整体效能及使用者满意度,为数控机床系统开发设计提供参考。     

Physics-Informed Multistage Machine Learning Strategy for the Nanomachining-Induced Plastic Deformation Behavior

The evolution of the dislocation density induced by the nanomachining process dominates the plastic deformation behaviors of materials, thus affecting the mechanical properties significantly. However, a challenging topic related to how to establish an accurate model for predicting the dislocation density based on the limited simulations and experiments arises due to the complicated thermal–mechanical coupling mechanism during the machining process. Herein, a multistage method integrating machine learning, physics, and high-throughput atomic simulation is proposed to investigate the effect of cutting speed on the dislocation behavior in polycrystal copper. Compared with the traditional one-step machine learning method, the constraint of physical features effectively improves the accuracy and generalization ability of the model. The results indicate that the dislocation behaviors depend on the competition between the cutting force and temperature. In the low-cutting speed, the predominated role of the cutting temperature leads to a rapid decline of the dislocation density. In contrast, the dislocation density tends to be stable under a high-speed cutting process due to the dynamic balance between the effects of the cutting force and temperature. Notably, the proposed strategy provides a new and universal framework to design the machining parameters to obtain high-quality products.     

Neural Machine Translation by Fusing Key Information of Text

When the Transformer proposed by Google in 2017, it was first used for machine translation tasks and achieved the state of the art at that time. Although the current neural machine translation model can generate high quality translation results, there are still mistranslations and omissions in the translation of key information of long sentences. On the other hand, the most important part in traditional translation tasks is the translation of key information. In the translation results, as long as the key information is translated accurately and completely, even if other parts of the results are translated incorrect, the final translation results’ quality can still be guaranteed. In order to solve the problem of mistranslation and missed translation effectively, and improve the accuracy and completeness of long sentence translation in machine translation, this paper proposes a key information fused neural machine translation model based on Transformer. The model proposed in this paper extracts the keywords of the source language text separately as the input of the encoder. After the same encoding as the source language text, it is fused with the output of the source language text encoded by the encoder, then the key information is processed and input into the decoder. With incorporating keyword information from the source language sentence, the model’s performance in the task of translating long sentences is very reliable. In order to verify the effectiveness of the method of fusion of key information proposed in this paper, a series of experiments were carried out on the verification set. The experimental results show that the Bilingual Evaluation Understudy (BLEU) score of the model proposed in this paper on the Workshop on Machine Translation (WMT) 2017 test dataset is higher than the BLEU score of Transformer proposed by Google on the WMT2017 test dataset. The experimental results show the advantages of the model proposed in this paper.     

智能化锅炉水冷壁清焦检测机具

电厂锅炉水冷壁清焦检测工作一直是个难题,目前国内外尚无更好的解决办法。多功能金属表面清焦检测机具为电厂锅炉水冷壁的清焦检测工作提供了智能化手段,使水冷壁的检测清焦自动化,较好地解决了这一难题。另外,它亦可广泛应用于船体检测、储油气罐及给排水、油管道的检测、疏通堵塞等领域。