Shenyang Institute of Automation,Chinese Academy Of Sciences
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Predicting Cardiovascular and Cerebrovascular Events Based on Instantaneous High-Order Singular Entropy and Deep Belief Network
No full textAutomatically predicting cardiovascular and cerebrovascular events (CCEs) is a key technology that can prevent deaths and disabilities. Herein, we propose predicting CCE occurrences based on heart rate variability (HRV) analysis and a deep belief network (DBN). The proposed prediction algorithm uses eight novel HRV signal features, which are calculated based on the following steps. First, the instantaneous amplitude (IA), instantaneous frequency (IF), and instantaneous phase (IP) are calculated for the HRV signals. Second, the high-order cumulant is estimated for the HRV and its IA, IF, and IP. Third, a high-order singular entropy is calculated to measure the fluctuation in signals. Fourth, eight novel features are obtained and processed using a DBN classifier designed for CCE prediction. The DBN classification method, with the novel HRV features, outperformed existing methods in terms of accuracy. Thus, the scheme proposed herein provided a novel direction for predicting CCEs.</p
A Safe and Compliant Noncontact Interactive Approach for Wheeled Walking Aid Robot
No full textAiming at promptly and accurately detecting falls and drag-to gaits induced by asynchronous human-robot movement speed during assisted walking, a noncontact interactive approach with generality, compliance and safety is proposed in this paper, and is applied to a wheeled walking aid robot. Firstly, the structure and the functions of the wheeled walking aid robot, including gait rehabilitation robot (GRR) and walking aid robot (WAR) are illustrated, and the characteristic futures of falls and the drag-to gait are shown by experiments. To obtain gait information, a multichannel proximity sensor array is developed, and a two-dimensional gait information detection system is established by combining four proximity sensors groups which are installed in the robot chassis. Additionally, a node-iterative fuzzy Petri net algorithm for abnormal gait recognition is proposed by generating the network trigger mechanism using the fuzzy membership function. It integrates the walking intention direction vector by taking gait deviation, frequency, and torso angle as input parameters of the system. Finally, to improve the compliance of the robot during human-robot interaction, a PID_SC controller is designed by integrating the gait speed compensation, which enables the WAR to track human gait closely. Abnormal gait recognition and assisted walking experiments are carried out respectively. Experimental results show that the proposed algorithm can accurately identify abnormal gaits of different groups of users with different walking habits, and the recognition rate of abnormal gait reaches 91.2%. Results also show that the developed method can guarantee safety in human robot interaction because of user gate follow-up accuracy and compliant movements. The noncontact interactive approach can be applied to robots with similar structure for usage in walking assistance and gait rehabilitation.</p
Suspension balance analysis and counterweight optimization design of AUV docking device
No full text为了保证用于对接回转型自主水下机器人（autonomous underwater vehicle,AUV）的开合式接驳装置处于水下单点系泊悬浮状态时的平衡与稳定，须对其进行配重优化设计。通过对开合对接机构的位移分析，获得接驳装置体重心和浮心位置的变化范围；基于静力学理论，建立并分析重心、浮心和拖点的相对位置与接驳装置悬浮平衡纵倾角的关系。为了使接驳装置悬浮平衡纵倾角及其波动幅值小，及配重后接驳装置的质量最小，建立AUV接驳装置配重优化模型，并采用序列二次规划（sequential quadratic programming,SQP）方法对配重铅块、浮力材的特征尺寸和拖点位置等配重参数进行优化设计。通过接驳装置单点系泊悬浮实验，验证了优化配重后接驳装置满足设计要求。研究结果表明：相比自由拖架形式，采用固定拖架形式可以增大拖点与重心之间的垂向距离，非常有利于装置的水下悬浮平衡；相比经验配重设计，优化配置后装置总质量约减小了13.4 kg，悬浮平衡纵倾角波动幅值约减小90.68%，稳心高约增加7.63 mm，配重优化后AUV接驳装置系泊悬浮平衡状态良好。配重优化模型的建立及分析结果对水下接驳拖体的衡重设计及其拖架方案设计具有一定的指导意义。</p
面向烟草行业的能源监控系统设计与应用
No full text在烟草行业，能源系统保证烟草生产的稳定和高效率运行，能源消耗成本在烟草生产成本中占比很大，直接影响企业的经济效益。该文对烟草行业能源进行分析，基于“互联网+”新技术，对能源信息采集、能源监控、能源管控、能源分析进行流程优化，设计并研发了能源监控系统，该系统已在某烟草企业应用，应用结果证明能有效提升烟草行业的能源管控。</p
Representative Task Self-Selection for Flexible Clustered Lifelong Learning
No full textConsider the lifelong machine learning paradigm whose objective is to learn a sequence of tasks depending on previous experiences, e.g., knowledge library or deep network weights. However, the knowledge libraries or deep networks for most recent lifelong learning models are of prescribed size and can degenerate the performance for both learned tasks and coming ones when facing with a new task environment (cluster). To address this challenge, we propose a novel incremental clustered lifelong learning framework with two knowledge libraries: feature learning library and model knowledge library, called Flexible Clustered Lifelong Learning (FCL3). Specifically, the feature learning library modeled by an autoencoder architecture maintains a set of representation common across all the observed tasks, and the model knowledge library can be self-selected by identifying and adding new representative models (clusters). When a new task arrives, our FCL3 model firstly transfers knowledge from these libraries to encode the new task, i.e., effectively and selectively soft-assigning this new task to multiple representative models over feature learning library. Then: 1) the new task with a higher outlier probability will be judged as a new representative, and used to redefine both feature learning library and representative models over time; or 2) the new task with lower outlier probability will only refine the feature learning library. For model optimization, we cast this lifelong learning problem as an alternating direction minimization problem as a new task comes. Finally, we evaluate the proposed framework by analyzing several multitask data sets, and the experimental results demonstrate that our FCL3 model can achieve better performance than most lifelong learning frameworks, even batch clustered multitask learning models
Unifying Classification and Bounding Box Regression Head For Object Detection
No full textAbstract
Object detection usually includes two parts: objection classification and location. At present, the popular object detectors usually use two detection heads: one head is used to predict classification score, and the other one is used to predict the bounding box (bbox), respectively. In this paper, we first stack classification head after feature extract convolutional neural networks of bbox regression head. Then, we establish the classification networks by using a bounding box feature. The bounding box feature is very useful when the classification head uses soft Intersection over Union (IoU) labels. In experiment parts, only using PASCAL VOC 2007 datasets, soft Centerness labels, and soft IoU labels get 50.06 mAP and 52.08 mAP on VOC 2007 test. Compared with FCOS, they have 1.08% and 1.12% improvements. Using PASCAL VOC 2007 and 2012 datasets, our Union A*B head gets 78.71mAP after 12 epochs training with ResNet-101 as backbone and FPN as the neck. Extensive experimental results show that the proposed algorithm is superior to other detection methods
一种基于服务机器人视觉功能检测方法
No full text本发明涉及一种基于服务机器人视觉功能检测方法，包括以下步骤：图像采集步骤：利用CCD工业相机分别拍摄室内和室外场景下的目标静态图像和连续多帧视频，发送给视觉处理器；图像预处理步骤：视觉处理器模块发送指令接收CCD工业相机采集的图像和视频并存储至图像数据库中，调用图像预处理模块对接收的图像进行处理使得原始图像和视频的产生比例变形或增加噪声，用于模拟真实场景下的图像变形和天气影响生成模拟图像；图像识别步骤：视觉处理器模块发送指令调用图像真值标注模块对模拟图像进行比对处理识别目标静态图像或视频，并当识别率达到预设阈值时判定该服务机器人视觉检测功能是否合格
一种链式多体自主水下机器人
No full text本发明属于水下机器人技术领域，特别涉及一种链式多体自主水下机器人。包括多个依次首尾铰接的单体AUV；单体AUV包括依次连接的艏部扩展舱段、舯部采样舱段、舯部垂推舱段、舯部控制舱段及艉部扩展舱段，其中舯部采样舱段用于采样；舯部垂推舱段用于提供沿竖直方向运动的推力；艉部扩展舱段用于提供前后运动及俯仰运动的动力。本发明具备动力分布式的特点,具有高效率、高机动航行的优势，能满足水下复杂环境任务需求，能够为执行深远海任务以及水下考古、管道运维等复杂环境作业提供潜在解决方案
用于高速切削刀具实验的机械装置
No full text本实用新型涉及实验装置，具体地说是一种用于高速切削刀具实验的机械装置，包括支撑台立杆机构、承载电机驱动机构及切削刀轴机构，支撑台立杆机构包括支撑台底座、支撑台立杆及支撑台立杆支座，支撑台立杆的下端与支撑台底座连接，上端与支撑台立杆支座相连；承载电机驱动机构包括电机底座、电机支座及驱动电机，电机底座的一侧安装于支撑台立杆支座上，另一侧与电机支座的下端固接，电机支座的上端安装有驱动电机；切削刀轴机构包括刀轴及联轴器，刀轴转动安装于电机底座上，刀轴的下端与高速切削刀具相连，上端通过联轴器与驱动电机的输出轴连接。本实用新型稳定性好，定位精度高，不需要很大的实验场地而且制作成本低，特别适合大多数小型实验
基于机器学习的设备异常分析研究
No full text文章通过对烟草企业生产线自动化生产设备的异常诊断问题进行分析，根据生产设备运行时生产数据的采集，选择了CNN和LSTM网络这两种机器学习的算法对设备异常诊断进行了研究。通过对仿真实验的数据对比，选择了LSTM网络作为设备异常诊断模型的核心算法。通过算法的仿真实验，以及烟草企业的实际需求，验证了LSTM网络算法在异常诊断上的可用性和先进性。在面对大量的实时设备运行数据时，能够快速地判断设备状况，并且在出现异常时能够迅速准确地分析出异常的类型，进而快速地制定设备异常解决方案，使设备能够快速地恢复正常生产状态，减少企业的经济损失。</p