Shenyang Institute of Automation,Chinese Academy Of Sciences
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Fabrication of flexible microfluidic pipes with embedded metal electrodes based on electrohydrodynamic jet printing
No full text微流控芯片在生物、化学、医学等领域受到了研究者们的广泛关注,尤其是含有金属电极的微流体管道在毛细电泳、电化学微量检测、生物医学工程和柔性电子领域具有广泛的需求前景.文章提出了一种简单按需制备阵列化嵌金属电极柔性微流体管道的方法.该方法基于电喷印直写技术并结合翻模和湿法刻蚀工艺,实现了嵌金属电极柔性微流体管道阵列的制备.首先,通过在线性转动接收基底上叠加直写聚乙烯醇(PVA)纤维,制备了可嵌入聚二甲基硅氧烷(PDMS)的表面光滑的线性凸起微结构(线宽为10～100μm,高宽比可大于1:2),并以此作为模板,实现了阵列化柔性微流体沟道的制造;其次,通过在平动接收基底上直写光刻胶作为保护层,并结合湿法刻蚀工艺,实现了在含有微流体沟道阵列的柔性基底上金属图案化导电电极(线宽低至5μm)的灵活制造;最后,对通入不同浓度盐溶液的微流体管道进行电学测试,验证了其管道的导通性和金属电极的导电性.结果表明:基于电喷印的集成制造流程可以灵活、简单、高效、低成本的按需加工阵列化嵌金属电极柔性微流体管道,有望应用在生物医学工程和柔性电子等领域.</p
Numerical simulation of the stability of water fiber-optic in water jet-guided laser machining
No full textWater jet-guided laser machining is a new compound machining technology, which has been widely used in many fields due to its better processing effect. In this technology, the coupling of laser beam and micro-water jet directly determines the machining effect, and the prerequisite for successful coupling is the steady flow of the water jet, so ensuring the stability of the micro-water jet is the key to the stable machining of water jet-guided laser. Therefore, it is of great significance to studying the stability of the water fiber-optic in water jet-guided laser processing. In this paper, aiming at the problem that the stability of the water fiber-optic is difficult to control, a finite element model of the water fiber-optic is established. The convection model is vortex gas-phase flow “enveloped” water fiber-optic which is used to explain the interaction mechanism, and the flow field distribution of gas-phase flow and water fiber-optic convection was obtained. The results show that water fiber-optic is refined under the constraint of gas-phase flow, and the maximum processing distance can increase by three times. At the same time, the gas-phase flow can accelerate the removal of processing debris, and the processing accuracy and efficiency are improved.</p
一种用于深海摄像机圆顶视窗的密封装置
No full text本发明属于水下机器人领域，具体地说是一种用于深海摄像机圆顶视窗的密封装置，包括玻璃球罩、球罩压盖、球罩隔垫、密封环、矩形补偿环及镜片组基座，其中球罩压盖套设于所述玻璃球罩的后端，并与所述镜片组基座的前端螺纹连接，所述镜片组基座的后端与摄像机舱体密封螺纹连接；所述球罩压盖与玻璃球罩之间装有密封环，所述密封环的后端延伸至球罩压盖与镜片组基座前端之间，所述球罩压盖与密封环之间安装有矩形补偿环；所述玻璃球罩的底部与镜片组基座之间设有球罩隔垫。本发明具有结构紧凑、可靠性高、易于实现等优点
一种升降系统及收放装置
No full text本申请公开了一种升降系统及收放装置，升降系统包括固定平台、活动平台和升降机构，所述活动平台通过所述升降机构与所述固定平台连接，所述升降机构包括：主动部，设置于所述固定平台上；导轨，沿竖直方向设置于所述固定平台上；从动部，设于所述导轨中，并且一端与所述主动部柔性连接，另一端与所述活动平台固定连接，所述主动部驱动所述从动部做上升或下降运动。本申请可以减少设备故障，提高设备的使用寿命
一种太赫兹精确聚焦透镜设计方法
No full text本发明涉及一种太赫兹精确聚焦透镜设计方法，包括以下步骤：太赫兹透镜工作频率和材料选取；太赫兹透镜材料折射率准确标定；太赫兹透镜通光孔径、焦距、边缘厚度和面型选取；太赫兹透镜聚焦焦移效应补偿；太赫兹透镜表面非球面优化。使用本发明而设计出的太赫兹透镜为非球面折射聚焦透镜，其真实焦距与理论焦距的偏差绝对值可控制在1％之内。本发明主要解决了目前商用太赫兹折射透镜在对太赫兹波进行聚焦时，因焦移效应导致实际焦距小于理论焦距，进而导致焦平面位置偏差较大的问题
基于非凸低秩张量近似的图像超分辨率重建系统
No full text本发明涉及基于非凸低秩张量近似的图像超分辨率重建系统。在医学领域，磁共振图像(MRI)、计算机断层扫描图像等诸多医学成像方面总受到医疗系统固有硬件、医学图像采集时间的制约而缺乏高分辨率(HR)的医学图像。针对医学图像超分(SR)问题，本发明利用耦合加权三维全变分(3DTV)的块张量非凸近似方法来提高医学图像的分辨率。首先，对输入的低分辨率图像进行块匹配操作形成4D块，然后采用非凸张量惩罚函数挖掘其中蕴含的低秩结构特性、非局部自相似性。采用加权的三维全变分正则项来挖掘医学图像数据的局部平滑特性。该方法解决了医学图像的分辨率增强、抑制噪声及图像细节恢复等问题并实验验证了在不同采样率、噪声水平下该算法的优越性和泛化性
Efficient weakly supervised LIBS feature selection method in quantitative analysis of iron ore slurry
No full textOn-stream analysis of the element content in ore slurry plays an important role in the control of the mineral flotation process. Therefore, our laboratory developed a LIBS-based slurry analyzer named LIBSlurry, which can monitor the iron content in slurries in real time. However, achieving high-precision quantitative analysis results of the slurries is challenging. In this paper, a weakly supervised feature selection method named spectral distance variable selection was proposed for the raw spectral data. This method utilizes the prior information that multiple spectra of the same slurry sample have the same reference concentration to assess the important weight of spectral features, and features selected by this prior can avoid over-fitting compared with a traditional wrapper method. The spectral data were collected on-stream of iron ore concentrate slurry samples during the mineral flotation process. The results show that the prediction accuracy is greatly improved compared with the full-spectrum input and other feature selection methods; the root mean square error of the prediction of iron content can be decreased to 0.75%, which helps to realize the successful application of the analyzer. (C) 2022 Optical Society of Americ
An Overview of Underwater Vision Enhancement: From Traditional Methods to Recent Deep Learning
No full textUnderwater video images, as the primary carriers of underwater information, play a vital role in human exploration and development of the ocean. Due to the optical characteristics of water bodies, underwater video images generally have problems such as color bias and unclear image quality, and image quality degradation is severe. Degenerated images have adverse effects on the visual tasks of underwater vehicles, such as recognition and detection. Therefore, it is vital to obtain high-quality underwater video images. Firstly, this paper analyzes the imaging principle of underwater images and the reasons for their decline in quality and briefly classifies various existing methods. Secondly, it focuses on the current popular deep learning technology in underwater image enhancement, and the underwater video enhancement technologies are also mentioned. It also introduces some standard underwater data sets, common video image evaluation indexes and underwater image specific indexes. Finally, this paper discusses possible future developments in this area
SiamOAN: Siamese object-aware network for real-time target tracking
No full textExisting Siamese-based tracking algorithms usually utilize local features to represent the object, which lack sufficient discrimination and may degrade tracking performance in challenging situations. To address this issue, we propose a novel object-aware network to improve feature representation and achieve robust object tracking. The proposed object-aware network contains a background filter module (BFM), channel complementary module (CCM), and template adaptive network (TAN). Specifically, by locating the target in the initial frame on the feature maps, BFM suppresses the background interference of the target template. CCM captures the global context by exploring the complementary information of each channel. The lightweight TAN adaptively recognizes valuable features for the target and represents the target template just through a single vector. Benefiting from these three components, the object-aware network enhances the discrimination of feature maps and alleviates background interference to some extent. The proposed object-aware network could be integrated with the Siamese-based backbone network for real-time object tracking, named SiamOAN. Extensive experiments on the six challenging benchmarks including OTB100, UAV123, VOT2016, VOT2018, GOT10k, and LaSOT, show that the pro-posed SiamOAN outperforms many state-of-the-art trackers and runs at approximately 67 fps on GPU RTX3090. CO 2021 Published by Elsevier B.V
CF-DAML: Distributed automated machine learning based on collaborative filtering
No full textThe search for a good machine learning (ML) model takes a long time and requires the considerations of many alternatives, including data preprocessing, algorithm selection, and hyperparameter tuning methods. Thus, tedious searches face a combinatorial explosion problem. In this work, we build a new automated machine learning (AutoML) system called CF-DAML, a distributed automated system based on collaborative filtering (CF), to address these challenges by recommending and training suitable models for supervised learning tasks. CF-DAML first computes some informative meta-features for a new dataset, then uses a weighted l(1)-norm (W1-norm) to accurately calculate the k nearest neighbors (kNN) of the new dataset, and finally recommends the top N models with good performances on each of its neighbors to the new dataset. We also design a distributed system (DSTM) for training the models to reduce the time complexity substantially. In addition, we develop a multilayer selective stacked ensemble system (MSSE), whose base models are selected from among suitable candidate models based on their runtimes, classification accuracies, and diversities, to enhance the stability of CF-DAML. To our knowledge, this is the first work to combine memory-based CF and the selective stacked ensemble to solve the AutoML problem. Extensive experiments are conducted on many UCI datasets and the comparative results demonstrate that our approach outperforms the current state-of-the-art methods.</p