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Research status and prospect of plate elements in absolute nodal coordinate formulation
No full textThe Absolute Nodal Coordinate Formulation (ANCF) is a milestone in the study of flexible multibody dynamics and is of great significance for the study of the dynamics of multi-flexible systems, of which the plate element is an important part. In this article, the construction and principles of this type of element are systematically traced, the types of elements that have been studied are summarized, and the research history of the element locking problem and extended applications in different fields are briefly described. Through the systematic summary, the shortcomings in the current research and application of the element are identified, and some suggestions for future theoretical research on the plate element are given. The functional expansion of the plate element under the conditions of constraints, materials and physical fields as well as practical engineering applications are discussed.</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
Medical image fusion via discrete stationary wavelet transform and an enhanced radial basis function neural network
No full textMedical image fusion of images obtained via different modes can expand the inherent information of original images, whereby the fused image has a superior ability to display details than the original sub images, to facilitate diagnosis and treatment selection. In medical image fusion, an inherent challenge is to effectively combine the most useful information and image details without information loss. Despite the many methods that have been proposed, the effective retention and presentation of information proves challenging. Therefore, we proposed and evaluated a novel image fusion method based on the discrete stationary wavelet transform (DSWT) and radial basis function neural network (RBFNN). First, we analyze the details or feature information of two images to be processed by DSWT by using two-level decomposition to separate each image into seven parts, comprising both high-frequency and low-frequency sub-bands. Considering the gradient and energy attributes of the target, we substituted the pending parts in the same position in the two images by using the proposed enhanced RBFNN. The input, hidden, and output layers of the neural network comprised 8, 40, and 1 neuron(s), respectively. From the seven neural networks, we obtained seven fused parts. Finally, through inverse wavelet transform, we obtained the final fused image. For the neural network training method, the hybrid adaptive gradient descent algorithm (AGDA) and gravitational search algorithm (GSA) were implemented. The final experimental results revealed that the novel method has significantly better performance than the current state-of-the-art methods. (C) 2022 Elsevier B.V. All rights reserved
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
Cloud Computing Based Demand Response Management using Deep Reinforcement Learning
No full textDemand response is an effective way for ensuring safety and stabilization of power grid by maintaining the balance between the supply and the demand of power grid, and this paper focuses on using electric water heaters for demand response. In addition to considering comfort and price factors as did in previous works, this paper considers the overshoot temperature and its influence on demand response. First, a theoretical model of the heating and cooling processes of the electric water heater is established; second, the demand response process using electric water heaters is analyzed, including the influences of the physical parameters and the settings of electric water heaters on the demand response process; third, a model is established considering the demand response requirement, the comfort of owners of electric water heaters, and the electricity price, simultaneously; fourth, an optimization method based on deep reinforcement learning is proposed for demand response using electric water heaters. Meanwhile, the influence of parameters on the results of demand response is discussed in details. Experimental results show the effectiveness of the proposed method</p
Fault detection and isolation of actuator failures in jet engines using adaptive dynamic programming
No full textThis paper presents a adaptive dynamic programming-based fault detection and isolation (FDI) scheme to detect and isolate faults in an aircraft jet engine. To this end, the weights in Actor-Critic neural networks are first tuned to learn the input-output map of the jet engine considering its multiple working modes. The convergences of the trainings in Critic-Actor neural networks are strictly proved without knowing the drift dynamics and the input dynamics in the presence of unknown nonlinearities and approximation errors. Using the residuals that are generated by measuring the difference of each network output and the measured engine output, various criteria are established for accomplishing the fault diagnosis task, that addresses the problem of fault detection and isolation of the system components. A number of simulation studies are carried out for combustion chamber of a single-spool jet engine to demonstrate and illustrate the advantages, capabilities, and performance of our proposed fault diagnosis scheme. (c) 2021 Elsevier Inc. All rights reserved
Brain-Inspired Fast Saliency-Based Filtering Algorithm for Ship Detection in High-Resolution SAR Images
No full textIn this article, we aim to improve the performance of synthetic aperture radar (SAR) ship detection under complex conditions. The complex backgrounds are commonly encountered for high-resolution (HR) SAR ship detection data set, and they greatly influence the detection performance of ships. In recent years, deep neural networks (DNNs) have made substantial improvements on detection by adopting data augmentation. However, the improvement is limited since the models are sensitive to noise. To address this problem, a Fast Saliency-based Filtering algorithm (FSF) is proposed to filter out interference information. The FSF method is inspired by the filtering mechanisms of the human brain, which help people filter out target-irrelevant information fast to better extract target-relevant information. The FSF includes two parts of the bottom-up process and the top-down process. The bottom-up process is used to extract a saliency map of an input image, and the other one is used to filter out target-irrelevant information based on the saliency map. The FSF can be a front-end preprocessing module of DNNs to fast filter out target-irrelevant information and obtain a primary priority map of an input image. Experimental results demonstrate that our brain-inspired FSF method obtains obvious improvement of detection performance on AIR-SARShip-1.0.</p
Codesign of Architecture, Control and Scheduling of Modular Cyber-Physical Production Systems for Design Space Exploration
No full textDesign space exploration (DSE) of cyber-physical production systems (CPPS) is a search problem in the space of potential compositional configurations. Current design methodologies follow the separated design paradigm in which the architecture, control, and scheduling are separately designed. Optimization of each part considers only the corresponding goals of interest and overlooks other aspects by adopting gross assumptions, which makes it difficult to determine the global optimal solution for a given system. To address this problem, we propose a codesign method that considers the design spaces of architecture, control and scheduling as monolithic, mixed discrete-continuous spaces. We formulate DSE as an optimization problem and propose a generic iterative algorithm scheme involving simulation in-the-loop to solve the above new problem. To illustrate the effectiveness of the proposed method, a real single-stage reducer CPPS is considered. The design results demonstrate that our method provides better solutions than does the separated design method.</p
Semi-supervised fault diagnosis method for chemical process based on TE-DS
No full text针对现有基于深度学习的化工过程故障诊断方法通常需要完备的标签数据才能构建故障诊断模型等局限，提出一种基于时间集成-双重学生模型(temporal ensembling-dual student,TE-DS)的半监督化工过程故障诊断方法。该方法首先以双重学生模型为基础，通过分类项约束、稳定性约束和一致性约束条件指导相互训练，有效地缓解了误差累积情况的发生。然后利用时间集成(temporal ensembling)将多个先前网络评估的预测集成作为一致性正则化对象，达到缓解预测值噪声、降低模型训练时间的目的，以提高分类性能，实现故障诊断。最后通过田纳西-伊斯曼(Tenessee-Eastman)化工过程基准数据进行故障诊断实验，验证提出方法的有效性和可行性，并与BNLSTM、DCNN和MCLSTM等有监督方法进行比较，证明了TE-DS算法对故障诊断的优越性。</p
Design and Dynamic Modeling of a Flexible Catcher for Noncooperative Targets
No full text为更好地实现对动态非合作目标的捕获，设计开发了一种多臂式柔性捕获器。这种捕获器的原理类似海葵等生物捕猎的方式，不依赖单个柔性臂的精准夹持而是靠多根臂所构成的臂群实现聚拢、挤压等动作，以完成对目标物体的捕获.基于能量守恒和动量守恒原理对非合作目标物体与柔性臂的碰撞问题进行分析，给出了发生碰撞后柔性臂与目标物体各自的运动参数.为进一步分析柔性臂的动态变形过程，采用多个线性关节和扭转关节的组合对单根柔性臂进行描述，并基于牛顿法对各离散关节进行受力分析，建立了柔性臂的动力学模型。将柔性臂整个变形过程离散为多个微小时间段运动的集合，通过动力学分析得到当前时刻的动力学参量，经过一个微小时间内的运动后即可得到下一时刻各质点的位置，迭代进行上述步骤便得到了柔性臂的动态变形过程。而后，通过实验确定单臂动力学模型的最优参数，并将参数优化后的动力学模型在不同加载情况下与单臂样机进行对比，验证了动力学模型的准确性。最后，在单臂动力学模型的基础上建立包括多根柔性臂的捕获器三维模型，进行非合作目标捕获的仿真与样机实验。结果表明：所设计的臂群式柔性捕获器能够很好地完成动态非合作目标捕获任务，所建立的捕获器三维仿真模型可以基本反映动态捕获过程。</p