Stream-Based Data Sampling Mechanism for Process Object

Process object is the instance of process. Vertexes and edges are in the graph of process object. There are different types of the object itself and the associations between object. For the large-scale data, there are many changes reflected. Recently, how to find appropriate real-time data for process object becomes a hot research topic. Data sampling is a kind of finding c hanges o f p rocess o bjects. There i s r equirements f or s ampling to be adaptive to underlying distribution of data stream. In this paper, we have proposed a adaptive data sampling mechanism to find a ppropriate d ata t o m odeling. F irst o f all, we use concept drift to make the partition of the life cycle of process object. Then, entity community detection is proposed to find changes. Finally, we propose stream-based real-time optimization of data sampling. Contributions of this paper are concept drift, community detection, and stream-based real-time computing. Experiments show the effectiveness and feasibility of our proposed adaptive data sampling mechanism for process object.     

Enhanced photoreactivity of MOFs by intercalating interlayer bands via simultaneous ?NCO and ?SCu modification

Herein, we propose a novel method to enhance the photoreactivity of an MOF catalyst by grafting isocyanate bonds ( NCO) and sulfhydryl-complexed copper ( SCu) onto ZIF-8 (NIF-SCu). The grafting process intercalated interlayer bands between the conduction and valence bands of ZIF-8, thereby providing a “ladder” for facile electron transition. The extreme improvement in the photoreactivity of NIF-SCu could be attributed to the enhancement in light responses in the range of 350–450 nm by  NCO groups and the widening of the visible light range of the MOF by  SCu groups. The formation of staggered energy levels in NIF-SCu could also narrow the band gap, lower the resistance, and facilitate the transfer of photogenerated carriers, thereby generating electrons with strong reduction potential in the  SCu conduction band. This study provides a new strategy for improving or even endowing the photoactivity of environmental functional materials with wide bandgaps.     

刚柔结合的充电机械臂设计

针对大型运输设备上的电池充电补给任务,设计了一款刚柔结合充电机械臂.针对机械臂的柔性部分和刚性部分分别分析了关节、任务、驱动空间之间的运动映射关系,给出了两级柔性臂的解耦运动学算法,使用蒙特卡洛方法求解了机械臂的工作空间,验证了机械臂设计的合理性,并用Adams软件进行动力学仿真分析,校核了设备选型的安全性.     

用于疏浚工程泥浆密度测量的电阻层析成像技术

江河湖海疏浚工程对于拓展和保障航运安全、促进生态恢复、维护/改善远海岛礁环境具有不可或缺的作用.目前,在绞吸和耙吸疏浚船上,多采用射线密度计及电磁流量计对输泥密度、产量等进行在线计量,并作为实现疏浚系统自动控制的重要参量.由于射线密度计潜在的人员健康、公共安全和环保风险,在一些国家和地区已建立了放射源的有序退出机制.电阻层析成像使用安装于管壁的电极阵列获得管道内流体的电导率分布,进而推导出管道内的泥浆密度,具有无辐射、全场测量及实时可视化的优点.电阻层析成像测量系统经过特殊设计,可适应从内陆淡水到海水的巨大电导率变化.初步测试表明,电阻层析成像测量数据与射线密度计具有良好一致性,具备了作为在线泥浆密度测量的可行性.此外,ERT可提供实时的流动成像,对于提高对疏浚管道监测能力和降低堵管概率具有重要意义.     

可重构机器人模块间装配误差识别方法研究

基于对可重构机器人配对接口间位姿误差的分析划分,研制模块间装配误差在线测量识别接口,建立配对接口位姿误差与接口几何结构偏差之间的关系模型,进而建立配对接口位姿误差与配对接口内部测距传感器测量值之间的映射关系模型,提出一种基于内部测距传感器位移量的模块间装配误差在线识别、补偿方法.为验证方法的正确性,研制单关节?连杆试验平台.试验结果表明装配误差经在线识别、补偿后,连杆末端的位置误差平均值减少7倍多.     

Super-Resolution Reconstruction of Images Based on Microarray Camera

In the field of images and imaging, super-resolution (SR) reconstruction of images is a technique that converts one or more low-resolution (LR) images into a highresolution (HR) image. The classical two types of SR methods are mainly based on applying a single image or multiple images captured by a single camera. Microarray camera has the characteristics of small size, multi views, and the possibility of applying to portable devices. It has become a research hotspot in image processing. In this paper, we propose a SR reconstruction of images based on a microarray camera for sharpening and registration processing of array images. The array images are interpolated to obtain a HR image initially followed by a convolution neural network (CNN) procedure for enhancement. The convolution layers of our convolution neural network are 3×3 or 1×1 layers, of which the 1×1 layers are used to improve the network performance particularly. A bottleneck structure is applied to reduce the parameter numbers of the nonlinear mapping and to improve the nonlinear capability of the whole network. Finally, we use a 3×3 deconvolution layer to significantly reduce the number of parameters compared to the deconvolution layer of FSRCNN-s. The experiments show that the proposed method can not only ameliorate effectively the texture quality of the target image based on the array images information, but also further enhance the quality of the initial high resolution image by the improved CNN.     

一种抗运动干扰的实时心率提取方法

针对基于容积脉搏波（PPG）提取运动心率时，传统心率提取算法由于运动噪声干扰使测量结果误差大、实时性不好的问题，提出一种抗运动干扰的实时心率提取方法。该方法通过实时小波去噪，同时结合三轴加速度信号（ACC）对运动进行分类训练，计算各运动状态心率增益，对实时心率值进行补偿。实验结果表明，通过与同时采集的ECG信号计算出的实时心率进行对比，绝对误差率仅为1.2%左右。相比传统心率提取算法，该算法具有抗干扰性强，实时准确的特点。