气动人工肌肉手臂的神经网络Smith预估控制

针对含时滞d的1关节气动人工肌肉(PAM)手臂,用三层递归神经网络(RNN),建立PAM手臂包含时滞的模型(即非线性Smith预估器),并超前d步预测PAM手臂的输出角度.将此超前d步的预测值作为反馈量,与设定值相比较得到的误差作为PID控制器输入量,实现Smith预估PID控制.同时每一步都用RNN模型当前时刻的输出值与PAM手臂当前时刻实际输出值之差的平方做为RNN权值的在线调整准则对RNN预测模型的权值进行在线调整,以自适应PAM手臂的不确定性和时变性.使用Matlab通过串口和研华亚当模块对实物PAM手臂进行控制,控制效果表明所提出的Smith预估PID控制算法比常规PID控制算法的性能有显著提高,证明所提出的算法是有效的和切实可行的.     

Distributed constraint optimization with MULBS: A case study on collaborative meeting scheduling

This paper introduces MULBS, a new DCOP (distributed constraint optimization problem) algorithm and also presents a DCOP formulation for scheduling of distributed meetings in collaborative environments. Scheduling in CSCWD can be seen as a DCOP where variables represent time slots and values are resources of a production system (machines, raw-materials, hardware components, etc.) or management system (meetings, project tasks, human resources, money, etc). Therefore, a DCOP algorithm must find a set of variable assignments that maximize an objective function taking constraints into account. However, it is well known that such problems are NP-complete and that more research must be done to obtain feasible and reliable computational approaches. Thus, DCOP emerges as a very promising technique: the search space is decomposed into smaller spaces and agents solve local problems, collaborating in order to achieve a global solution. We show with empirical experiments that MULBS outperforms some of the state-of-the-art algorithms for DCOP, guaranteeing high quality solutions using less computational resources for the distributed meeting scheduling task.     

人工神经网络技术在电工产品可靠性实验中的应用(理论部分)

本文简要介绍了人工神经网络，BP神经网络的结构、训练和学习规则以及人工神经网络非线性时间序列预测的应用概况。     

A Novel Artificially Humanized Anti-Cripto-1 Antibody Suppressing Cancer Cell Growth

Cripto-1 is a member of the EGF-CFC/FRL1/Cryptic family and is involved in embryonic development and carcinogenesis. We designed a novel anti-Cripto-1 artificial antibody and assessed the recognition to the antigen and the potential to suppress the growth of cancer stem cells. First, single chain antibody clones were isolated by bio-panning with the affinity to recombinant Cripto-1 protein from our original phage-display library. Then, the variable regions of heavy chain VH and light chain VL in each clone were fused to constant regions of heavy chain CH and light chain CL regions respectively. These fused genes were expressed in ExpiCHO-S cells to produce artificial humanized antibodies against Cripto-1. After evaluation of the expression levels, one clone was selected and the anti-Cripto-1 antibody was produced and purified. The purified antibody showed affinity to recombinant Cripto-1 at 1.1 pmol and immunoreactivity to cancer tissues and cell lines. The antibody was available to detect the immunoreactivity in tissue microarrays of malignant tumors as well as in Cripto-1 overexpressing cells. Simultaneously, the antibody exhibited the potential to suppress the growth of human colon cancer derived GEO cells overexpressing Cripto-1 with IC₅₀ at approximately 110 nM. The artificially humanized antibody is proposed to be a good candidate to target cancer cells overexpressing Cripto-1.     

敏捷化AIOps框架及运维数据质量评估方法

敏捷化智能运维(AIOps)框架将模型构建提前至测试阶段,利用该阶段产生的监控数据代替线上采集的数据以训练AIOps模型,进而实现智能运维的早开发与早使用. 运维数据质量评估方法通过最大均值差异度量方式,分别在健康评估与故障诊断运维场景下对训练数据分别进行趋势性、阶段性、可检测性及可诊断性评估,以预估数据对模型的适用性. 基于华为技术有限公司提供的测试环境设置测试用例并构建实验数据集,在该数据集上的实验结果验证了敏捷化AIOps框架的可行性及数据质量评估方法的有效性.     

强度传输方程和神经网络融合的三维重构算法

针对原有强度传输方程法所恢复的相位精度不够精确的缺点,提出强度传输方程和神经网络融合的三维重构算法. 利用强度传输方程求解出物体不同角度的初始相位,利用神经网络算法进行优化,根据不同角度的最终恢复相位结合乘法技术重构出三维体信息. 该算法具有精度高的特点,可以为三维成像技术的应用提供参考. 对于实验中的示例图像,该算法将强度传输方程得到的相位误差从21.40%降低为5.26%,重构三维物体与模拟真实物体的相关程度为显著相关.     

基于数据融合的ABC-SVM社区疾病预测方法

针对现有社区医疗服务中的疾病预测方法存在数据利用率低、疾病分析类型单一、自动化程度差、疾病预测效果不理想等不足,提出在物联网大数据环境下可用于社区医疗的健康数据融合及疾病预测方法. 通过主成分分析(PCA)和聚类分析对社区中居民的生理指标数据进行特征提取;结合人工蜂群(ABC)算法构造支持向量机(SVM)非线性分类器对数据进行特征级融合分析并预测潜在疾病. 实验结果表明,所提方法的疾病识别准确率达到93.10%,相较于传统SVM方法和BP神经网络方法分别提高17.24% 和72.41%. 该方法能够在提高数据利用率、降低计算资源消耗的前提下有效识别多种潜在疾病,可实现疾病早发现、早预防、早治疗;可广泛应用于社区健康管理、老年社区监护甚至临床医疗.     

人工智能对国内劳动就业影响研究综述

利用citespace关键词共现分析方法,梳理了1980年以来人工智能对劳动就业影响的国内相关文献,发现目前的研究议题主要包括人工智能影响就业的机制、人工智能对就业规模的影响、人工智能对收入分配的影响、人工智能对就业形态的影响、人工智能对劳动过程与劳动关系的影响. 总体来看,目前的研究主要集中在与劳动力市场相关的议题; 理论性的论述占大多数,而具有说服力的实证研究较少; 宏观性的研究较多,深入企业与生产现场考察的微观研究较少. 未来应进一步拓展研究主题,进行多种视角与跨学科研究,重视实证研究与微观研究.     

虚拟环境下机床主传动系统故障诊断的仿真实现

本文介绍了用AutoCAD完成机床主传动系统三维实体造型，用VisualC 、Visual LISP等工具完成主轴箱轴承、齿轮、轴等部位在运行中出现故障时的立体动画。经压电晶体加速度传感器实时检测机床的振动信号，对这些信号进行分析(统计处理、频谱分析等)提取出故障特征，经人工神经网络、模糊决策及专家系统等手段进行决策，判定故障部位和程度，可在微机上直观地观察主轴箱在运行过程中，各故障具体部位及损伤程度的虚拟现实情况。     

基于危险理论的人工免疫原理与应用*

侧重以危险理论的基本机制为线索,对危险理论的研究现状加以系统论述.首先以危险理论的免疫模型入手,归纳提炼出仿真机理并建立起隐喻算法;介绍其中具有代表性领域中的应用情况;最后对基于危险理论的人工免疫研究的下一步工作进行展望.