Shenyang Institute of Automation,Chinese Academy Of Sciences
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Bubble-based microrobots enable digital assembly of heterogeneous microtissue modules
No full textThe specific spatial distribution of tissue generates a heterogeneous micromechanical environment that provides ideal conditions for diverse functions such as regeneration and angiogenesis. However, to manufacture microscale multicellular heterogeneous tissue modules in vitro and then assemble them into specific functional units is still a challenging task. In this study, a novel method for the digital assembly of heterogeneous microtissue modules is proposed. This technique utilizes the flexibility of digital micromirror device-based optical projection lithography and the manipulability of bubble-based microrobots in a liquid environment. The results indicate that multicellular microstructures can be fabricated by increasing the inlets of the microfluidic chip. Upon altering the exposure time, the Young's modulus of the entire module and different regions of each module can be fine-tuned to mimic normal tissue. The surface morphology, mechanical properties, and internal structure of the constructed bionic peritoneum were similar to those of the real peritoneum. Overall, this work demonstrates the potential of this system to produce and control the posture of modules and simulate peritoneal metastasis using reconfigurable manipulation.</p
Theoretical investigation and implementation of nonlinear material removal depth strategy for robot automatic grinding aviation blade
No full textDue to the requirements of manufacturing accuracy and surface quality consistency, which will influence the dynamic properties and service cycle of aircraft engines, the high-precision material removal depth model is urgently needed for robot automatic grinding aviation blade system. This research developed a novel material removal depth (MRD) model to ensure quantitative grinding depth point by point for robot automatic grinding aviation blade. Firstly, the relationship between contact stress and contact force is developed based on simulation and theoretical derivation, and the contact contour is further investigated by the experiments. Then, the nonlinear material removal depth model is established to predict grinding depth according to Preston equation. Meanwhile, multiple linear regression method is introduced to analyze parameters of material removal depth model. The previous researches show that grinding parameters have strong correlation with MRD, especially for the non-negligible contact force consideration. Therefore, the non-uniform material removal with variable contact force strategy is developed according to the proposed model, and the proposed strategy is applied to the robot automatic grinding aviation blade system to achieve the quantitative grinding depth point by point on the aviation blade surface. The practice experiments further prove that the developed strategy is feasible and effective. Comparing the predicted and experimental MRD, the maximum errors and average relative errors are respectively 9.21% and 4.68%, when the aviation blade is ground by the proposed material removal strategy. Simultaneously, the experimental results tend to produce much better surface roughness with uniform surface texture which satisfies grinding requirements.</p
A hierarchical conditional random field-based attention mechanism approach for gastric histopathology image classification
No full textIn the Gastric Histopathology Image Classification (GHIC) tasks, which are usually weakly supervised learning missions, there is inevitably redundant information in the images. Therefore, designing networks that can focus on distinguishing features has become a popular research topic. In this paper, to accomplish the tasks of GHIC superiorly and assist pathologists in clinical diagnosis, an intelligent Hierarchical Conditional Random Field based Attention Mechanism (HCRF-AM) model is proposed. The HCRF-AM model consists of an Attention Mechanism (AM) module and an Image Classification (IC) module. In the AM module, an HCRF model is built to extract attention regions. In the IC module, a Convolutional Neural Network (CNN) model is trained with the attention regions selected, and then an algorithm called Classification Probability-based Ensemble Learning is applied to obtain the image-level results from the patch-level output of the CNN. In the experiment, a classification specificity of 96.67% is achieved on a gastric histopathology dataset with 700 images. Our HCRF-AM model demonstrates high classification performance and shows its effectiveness and future potential in the GHIC field. In addition, the AM module and transfer learning technique allow the network to generalize well to other types of image data except histopathology images, and we obtain 95.5% and 95.8% accuracies on IG02 and Oxford-IIIT Pet Datasets.</p
Application of Knowledge Atlas in Chemical Safety Field
No full text当前，化工安全领域越来越受到人们的重视，正处在从信息化建设向智能化迈进的关键时刻，以大数据和人工智能为代表的网络新技术已促进了化工安全领域的智能化发展。但是由于化工安全领域数据信息的繁杂性，难以对其进行统一而高效地收集、信息挖掘。本文将知识图谱技术引入化工安全领域，通过介绍知识图谱技术的相关概念以及构建流程，为如何面向化工安全领域构建知识图谱提供了思路。</p
A View Planning Method for 3D Reconstruction with Unknown Feature Prediction
No full textActive reconstruction can be considered as an intelligent perception problem that seeks to optimize the efficiency of the modeling tasks by adjusting the configuration of vision system on robotic platform automatically. We consider a feature as several local surfaces that have the same kinds of characteristics. A standard geometry can be defined by attributes abstracted from a set of features. For the regular geometry of the same category, descriptions and topological relations about these objects' features are known before modeling tasks. In order to maximize the acquisition of an object's surface in a few views, knowledge of its features is employed as partial prior information that guides the view planning in active reconstruction. Since each reconstruction task may have a different type of object to be rebuilt, in order to make the view planning algorithm suitable for different tasks, we store the prior information of the object in a fixed-form database, so as to call the content of the library to perform verification and reasoning of all features, and make predictions on unknown local surface and rough shape of the object. The predicted unknown surface is discretized and then marked as predicted voxels in the voxel space, which are used to evaluate the priority of all candidate viewpoints, together with the detected voxels. A novel hybrid optimization function is proposed that takes the constraint on overlap, the accessibility of predicted surface, and the amount of unknown object surface into account. Simulated experiments are conducted and the results show that our method is efficient for reconstruction
Friction induced vibration and energy generation study of two-degree-of-freedom piezoelectric coupled system
No full textFriction induced vibration (FIV) and its application for energy generation through two-degree-of-freedom (2-DOF) piezoelectric coupled structure are modelled, described, and studied in this work. The FIV mathematical model of 2-DOF system in the direction of friction is established. A 2-DOF energy generator is designed as a sample structure to transform single direction continuum friction motion to high frequency vibration for energy generation. During the operation of a moving plate compressed and sliding on top of the friction excitation module, varying friction force and contact status (sliding and stick) will lead to the dynamic piezoelectric compression deformation, which can generate continuum electrical power for energy absorbing and harvesting applications. Through numerical simulation and parameter studies, the influences of different stiffness ratios, mass ratios, normal force, velocity, acceleration, structure parameter and friction coefficients on the system power generation are analyzed. Through simple structural design, W level power generation can be obtained with few piezoelectric materials. This work revealed the possibility of transforming low frequency excitation to high frequency vibration of general 2-DOF structure for energy generation.</p
Obstructive Sleep Apnea Detection Scheme Based on Manually Generated Features andParallel Heterogeneous Deep Learning Model under IoMT
No full textObstructive sleep apnea (OSA) syndrome is a common sleep disorder and a key cause of cardiovascular and cerebrovascular diseases that seriously affect the lives and health of people. The development of Internet of Medical Things (IoMT) has enabled the remote diagnosis of OSA. The physiological signals of human sleep are sent to the cloud or medical facilities through Internet of Things, after which diagnostic models are employed for OSA detection. In order to improve the detection accuracy of OSA, in this study, a novel OSA detection system based on manually generated features and utilizing aparallel heterogeneous deep learning model in the context of IoMT is proposed, and the accuracy of the proposed diagnostic model is investigated. The OSA recognition scheme used in our model is based on short-term heart rate variability (HRV) signals extracted from ECG signals. First, the HRV signals and the linear and nonlinear features of HRV are combined into a one-dimensional (1-D) sequence. Simultaneously, a two-dimensional (2-D) HRV time-frequency spectrum image is obtained. The 1-D data sequences and 2-D images are coded in different branches of the proposed deep learning network for OSA diagnosis. To validate the performance of the proposed scheme, the Physionet ApneaECG public database is used. The proposed scheme outperforms the existing methods in terms of accuracy and provides a novel direction for OSA recognition.</p
载人模拟器控制及仿真训练装置及训练方法
No full text本发明涉及载人模拟器训练领域，具体地说是载人模拟器控制及仿真训练装置及训练方法。包括：设于载人模拟器舱内的舱内单元、设于载人模拟器舱外的舱外单元、仿真系统以及教控系统；舱内单元，用于将控制指令进行解析后得到控制信号，发送至舱外单元，以控制舱外单元实体设备运行、以及将控制信号发送至仿真系统进行仿真虚拟设备执行；同时，接收舱外单元实体设备的状态信息，以及接收仿真系统仿真后虚拟设备的虚拟信号和教控指令；本发明的仿真系统通过虚拟仿真功能代替真实传感器及检测装置，降低成本，配置灵活，与控制系统相结合，能够真实体现实艇潜水器的工作环境
面向固定翼无人机海上回收的拦阻机构
No full text本实用新型涉及一种面向固定翼无人机海上回收的拦阻机构，其中回收绳高度调节组件设有滑块，且所述滑块上设有回收绳转向组件，回收绳两端分别绕过对应侧的回收绳转向组件后绕置于对应侧的回收绳收放组件中；回收绳高度调节组件包括皮带、调节驱动电机和减摇控制模块，皮带通过调节驱动电机驱动移动，且调节驱动电机通过减摇控制模块控制转速，滑块与皮带一侧固连；回收绳收放组件包括绕线轮、收放驱动电机和柔性变阻尼控制模块，回收绳缠绕于绕线轮上，且绕线轮通过收放驱动电机驱动转动，收放驱动电机通过柔性变阻尼控制模块控制转速。本实用新型可以降低甲板摇摆对无人机回收的影响，确保无人机水上回收过程中的控制精度
一种重力流排水管下沉式水质自动监测设备
No full text本实用新型公开了一种重力流排水管下沉式水质自动监测设备，包括监测井和一体化水质监测柜体；所述一体化水质监测柜体包括：用于监测重力流排水管道中污水水质指标的水质监测单元、用于为重力流排水管下沉式水质自动监测设备运行提供电能的供电单元、用于控制水质监测单元自动运行及发送采集监控数据的控制与通讯单元。本实用新型实现对地埋式重力流排水管道中污水多水质指标的在线监测，对排放管网的异常污水快速识别，为污水处理厂提前预警，实现前置管理，辅助污水处理系统稳定运行，应用灵活，维护方便，解决了以往人工定期采样分析的时效性差、成本高的问题，填补了目前重力流排水管道污水水质在线监测与异常诊断的技术空白