等离子喷焊铜基自熔性合金的研制

为了克服铜铁异种材料喷焊过程中易产生焊缝裂纹和基体裂纹的倾向,研制开发了一种Cu-Ni-Sn自熔性合金粉末材料.通过对Cu-Ni-Sn粉末材料的化学成分的设计,喷焊层的组织显微结构及抗腐蚀性能的综合分析,认为该材料等离子喷焊工艺性能良好,焊层耐腐蚀效果十分明显.在生产实际中应用等离子喷焊制备Cu-Ni-Sn焊层,能明显提高粉末材料的熔敷率和性能,并且改善劳动工作环境.该合金性能优于传统材料,值得推广.     

变极性等离子弧焊铝合金厚板工艺研究

变极性等离子弧焊是一种新型焊接方法,由于等离子弧对焊接工艺和规范参数的变化比较敏感,获得良好焊缝接头的合理规范参数范围窄、裕度小.本文通过工艺试验,探讨了变极性等离子弧焊16mm铝合金厚板的技术可行性,论述了其相对于传统弧焊工艺的优点.     

数控机床的联网通讯与程序管理应用实例

介绍了数控DNC的功能特点,机床联网的实施方案、系统维护以及应用实例。     

Research on virtual dynamic optimization design for NC machine tools

Virtual dynamic optimization design can avoid the repeated process from design to trial-manufacture and test. The designer can analyze and optimize the product structures in virtual visualization environment. The design cycle is shortened and the cost is reduced. The paper analyzed the peculiarity of virtual optimization design, and put forwards the thought and flow to implement virtual optimization design. The example to optimize the internal grinder was studied via establishing precise finite element model, modifying the layout of Stiffened Plates and designing parameters of the worktable, and using the technology of modal frequency revision and the technology of multiple tuned damper. The result of optimization design compared the new grinder with the original grinder shows that the entire machine＇s first orders natural frequency is enhanced by 17%, and the response displacement of the grinding-head has dropped by 28% under the first order natural frequency and by 41% under second order natural frequency. Finally, the dynamic performance of the internal grinder was optimized.     

EBPSK调制通信系统中非线性检测的研究

主要讨论扩展的二元相移键控（EBPSK）调制通信系统中的检测技术,以实现高频谱利用率的同时降低能耗。针对高效调制信号码元间差异性较小的特点,接收机利用特殊的冲击滤波器（SIF）将相位变化转化为寄生调幅,增加码元间的差异性,提高能量利用率。同时,引入了支持向量机（SVM）非线性检测方法,解决EBPSK系统中门限检测性能难以达到最优的问题,并给出了维数较少的统一特征向量选取方法。为了减少计算量,选择适用于EBPSK系统的SVM核函数并优化计算方法。仿真结果表明,SVM非线性检测在较少的特征向量维数和低采样率的条件下,可获得最多7 dB的信噪比增益。因而,在EBPSK调制通信系统中选择SVM方法不仅可以提升检测性能,而且抗干扰能力更强,对采样率的鲁棒性更好。     

基于宽度网络架构的单模型主导联邦学习

联邦学习是一种分布式机器学习方法,它将数据保留在本地,仅将计算结果上传到客户端,从而提高了模型传递与聚合的效率和安全性.然而,联邦学习面临的一个重要挑战是,上传的模型大小日益增加,大量参数多次迭代,给通信能力不足的小型设备带来了困难.因此在本文中,客户端和服务器被设置为仅一次的互相通信机会.联邦学习中的另一个挑战是,客户端之间的数据规模并不相同.在不平衡数据场景下,服务器的模型聚合将变得低效.为了解决这些问题,本文提出了一个仅需一轮通信的轻量级联邦学习框架,在联邦宽度学习中设计了一种聚合策略算法,即FBL-LD.算法在单轮通信中收集可靠的模型并选出主导模型,通过验证集合理地调整其他模型的参与权重来泛化联邦模型. FBL-LD利用有限的通信资源保持了高效的聚合.实验结果表明, FBL-LD相比同类联邦宽度学习算法具有更小的开销和更高的精度,并且对不平衡数据问题具有鲁棒性.     

基于图神经网络的最大化代数连通度算法

随着智能体数量的增加, 多智能体系统中潜在的通信链路数量呈指数级增长. 过多冗余链路的存在给系统带来了大量的能源浪费和维护成本, 而盲目地去除链路又会降低系统的稳定性和安全性. 代数连通度是衡量图连通性的重要指标之一. 然而, 传统的半正定规划(SDP)方法和启发式算法在求解大规模场景下的最大化代数连通度问题时非常耗时. 在本文中, 我们提出了一种监督式的图神经网络模型来优化多智能体系统的代数连通度. 我们将传统的SDP方法应用于小规模任务场景中, 得到足够丰富的训练样本和标签. 在此基础上, 我们训练了一个图神经网络模型, 该模型可用于更大规模的任务场景中. 实验结果表明, 当需要去除15条边时, 我们的模型的平均性能达到了传统SDP方法的98.39%. 此外, 我们的模型计算时间极其有限, 可以推广到实时场景中去.     

Proteomic Biomarkers of the Apnea Hypopnea Index and Obstructive Sleep Apnea: Insights into the Pathophysiology of Presence,Severity, and Treatment Response

Obstructive sleep apnea (OSA), a disease associated with excessive sleepiness and increased cardiovascular risk, affects an estimated 1 billion people worldwide. The present study examined proteomic biomarkers indicative of presence, severity, and treatment response in OSA. Participants (n = 1391) of the Stanford Technology Analytics and Genomics in Sleep study had blood collected and completed an overnight polysomnography for scoring the apnea–hypopnea index (AHI). A highly multiplexed aptamer-based array (SomaScan) was used to quantify 5000 proteins in all plasma samples. Two separate intervention-based cohorts with sleep apnea (n = 41) provided samples pre- and post-continuous/positive airway pressure (CPAP/PAP). Multivariate analyses identified 84 proteins (47 positively, 37 negatively) associated with AHI after correction for multiple testing. Of the top 15 features from a machine learning classifier for AHI ≥ 15 vs. AHI < 15 (Area Under the Curve (AUC) = 0.74), 8 were significant markers of both AHI and OSA from multivariate analyses. Exploration of pre- and post-intervention analysis identified 5 of the 84 proteins to be significantly decreased following CPAP/PAP treatment, with pathways involving endothelial function, blood coagulation, and inflammatory response. The present study identified PAI-1, tPA, and sE-Selectin as key biomarkers and suggests that endothelial dysfunction and increased coagulopathy are important consequences of OSA, which may explain the association with cardiovascular disease and stroke.     

Warning: statistical benchmarking is addictive. Kicking the habit in machine learning

Algorithm performance evaluation is so entrenched in the machine learning community that one could call it an addiction. Like most addictions, it is harmful and very difficult to give up. It is harmful because it has serious limitations. Yet, we have great faith in practicing it in a ritualistic manner: we follow a fixed set of rules telling us the measure, the data sets and the statistical test to use. When we read a paper, even as reviewers, we are not sufficiently critical of results that follow these rules. Here, we will debate what are the limitations and how to best address them. This article may not cure the addiction but hopefully it will be a good first step along that road.     

A simulated annealing algorithm for multi-agent systems: a job-shop scheduling application

In this paper, a parallel implementation of the modular simulated annealing algorithm for classical job-shop scheduling is presented. The implementation is for a multi agent system running on the distributed resource machine, which is a novel, scalable, distributed virtual machine based on Java technology. The problems tackled are well known, difficult benchmarks, widely used to measure the efficiency of metaheuristics with respect to both the quality of the solutions and the central processing unit time. The empirical results obtained show that the method proposed is successful in comparison with a sequential version of modular simulated annealing algorithm and other methods described in the literature.