Institutional Repository of Institute of Automation, CAS
Not a member yet
23433 research outputs found
Sort by
Routing failure prediction and repairing for AUV-assisted underwater acoustic sensor networks in uncertain ocean environments
No full textUnderwater acoustic sensor networks (UASNs) provide temporary links, which is of great significance when it comes to dealing with abnormal situations or emergencies in Internet of underwater things (IoUT). However, UASNs are susceptible to changes and uncertainties in network topology, channel conditions, etc., which can easily lead to frequent link interruptions. In this paper, we introduce a link failure prediction mechanism and an autonomous underwater vehicle (AUV)-assisted routing holes repairing mechanism for routing design of UASNs in uncertain ocean environments, to save system energy consumption and improve network connectivity. The proposed link failure prediction mechanism takes into account residual energy of sensor nodes, node drifting information, and uncertain ocean ambient noise. When the energy of multiple sensor nodes is exhausted, the particle swarm optimization algorithm (PSO) is adopted to calculate the optimal repair location, and an AUV is used for fixed point repairing. The proposed method can effectively reduce the energy consumption of sensor nodes, increase the packet delivery ratio, and extend the life of entire network of UASNs. (C) 2021 Elsevier Ltd. All rights reserved
Review on Endogenous Information Security Technology of Industrial Measurement and Control Equipment
No full text工业测控设备是工业控制系统的神经中枢，其信息安全问题直接关系到工业控制系统的安全。传统信息安全防护技术手段具有局限性，网络无法阻断物理介质传输数据和物理设备的接入，即使是物理隔离的工业测控设备，亦可以成为攻击目标，迫切需要增强工业测控设备自身的内生安全防护能力。本文结合相关国际标准和国家标准，将工业测控设备内生信息安全防护技术分为静态加固技术和动态防护技术，对涉及的七类信息安全防护技术进行了逻辑分类，给出了工业测控设备的内生信息安全的术语定义，并具体分析、评价了已有相关理论研究和关键技术的优势和不足之处。最后，对工业测控设备的内生信息安全防护技术的发展趋势进行了展望。</p
Context-wise attention-guided network for single image deraining
No full textIn this paper, we propose a context-wise attention-guided network for single image deraining. Unlike most existing deraining methods, our network exploits underlying complementary information not only across multiple scales but also between levels. Specifically, our network architecture is designed to transmit the inter-level and inter-scale features. To extract guiding information and improve the discriminating ability of context-wise attention-guided network, we propose a net-context-wise attention module to generate attention maps. Following residual learning, the clean image is created by removing the predicted rain streak layer from the rainy input. Experimental results show our method has better performance on public datasets than some state-of-the-art methods.</p
A method for obtaining the fraction of absorbed energy of material based on laser shock processing experiment and simulation
No full textFraction of absorbed energy (FAE) is an important parameter to determine the plasma shock wave pressure. With the purpose of obtaining the FAE of material and accurately calculating the plasma shock wave pressure, a method based on laser shock processing (LSP) experiment and finite element simulation was proposed in this work. The Ni-based superalloy GH4169 was selected as experimental material, and the experimental sample was treated by single-point LSP. The residual stress of experimental sample after LSP treatment was determined using sin2ψ method by X-ray residual stress device. In finite element simulation, the initial value of FAE was assumed as 0.1, and then, the LSP finite element simulation was performed with the change of FAE until the results obtained by LSP experiment and simulation were fell into an allowable range. Based on this method, the FAE with 0.13 for Ni-based superalloy GH4169 was obtained. This work can enrich the theory of LSP and provide theoretical guidance for researchers to obtain the accurate FAE of materials.</p
Self-adaptive coding for spiking neural network
No full text脉冲神经网络(SNN)采用脉冲序列表征和传递信息，与传统人工神经网络相比，更具有生物可解释性。但典型SNN的特征提取能力受到其结构限制，对于图像数据等多分类任务的识别准确率不高，不能与卷积神经网络(CNN)相媲美。针对该问题，提出了一种新型的自适应编码脉冲神经网络(SCSNN)，将CNN的特征提取能力和SNN的生物可解释性结合起来，采用生物神经元动态脉冲触发特性构建网络结构，并设计了一种新的替代梯度反向传播方法直接训练网络参数。所提出的SCSNN网络分别在MNIST数据集和Fashion-MNIST数据集做了验证，取得较好的识别结果，在MNIST数据集上准确率达到了99.62%，在Fashion-MNIST数据集上准确率达到了93.52%，验证了本模型的有效性。</p
Novel power-exponent-type modified RNN for RMP scheme of redundant manipulators with noise and physical constraints
No full textNoise and physical constraints of redundant manipulators are the two major challenges in the repetitive motion planning (RMP) problems. Therefore, this paper proposed a power-exponent-type modified recurrent neural network (PET-MRNN) to simultaneously address both noise and physical constraints. Moreover, PET-MRNN model is activated by a new Sbp-sinh type nonlinear activation function proposed in this paper. The Sbp-sinh type activation function is first applied to such time varying quadratic program (TVQP) solving and possesses excellent convergence performance. Theoretical analysis proves that the PET-MRNN model can completely eliminate noise disturbance through learning and compensation during the convergence process, and then converge the residual error to zero and obtain the theoretical solution. Finally, simulation and experiments further proved the superiority of the PET-MRNN and the Sbp-sinh type activation function.</p
Multifunctional thermo-magnetically actuated hybrid soft millirobot based on 4D printing
No full textSoft materials, which use both internal energy change and external energy supply to produce shape morphing and motion, are essential for the development of robotics. Four-dimensional (4D) printing is a promising method for fabricating soft robots with arbitrary structures. However, there are still few hybrid soft robots that can be manufactured by 4D printing because of the physicochemical nature of the materials. In this study, a novel smart hydrogel composed of NIPAM, Laponite nanoclay, and NdFeB magnetic particles, which have simultaneous temperature sensation and magnetic actuation, was synthesized for 4D printing of robots. It has been proven that this material has good mechanical properties and excellent machinability and biocompatibility. Soft millirobots with different structures and functions were printed, including a catheter with a multi-segment magnetic head, a leptasteria-like robot, and a shellfish-like robot, which can respond to both magnetic and thermal fields. The locomotion of the millirobot has been verified to overcome physical obstacles in the human stomach model and complete active transportation of cargo. The synergistic responses to the magnetic field and thermal field make the robot more adaptable and reduce the leakage of drugs during transportation. The 4D printed soft millirobots will promote the application prospects of robots in the fields of bioengineering and medical treatment.</p
Research on the integrated manipulator of point cloud measurement and precise cutting for waste nuclear tank
No full textPurpose Nuclear waste tanks need to be cut into pieces before they can be safely disposed of, but the cutting process produces a large amount of aerosols with radiation, which is very harmful to the health of the operator. The purpose of this paper is to establish an intelligent strategy for an integrated robot designed for measurement and cutting, which can accurately identify and cut unknown nuclear waste tanks and realize autonomous precise processing. Design/methodology/approach A robot system integrating point cloud measurement and plasma cutting is designed in this paper. First, accurate calibration methods for the robot, tool and hand-eye system are established. Second, for eliminating the extremely scattered point cloud caused by metal surface refraction, an omnidirectional octree data structure with 26 vectors is proposed to extract the point cloud model more accurately. Then, a minimum bounding box is calculated for limiting the local area to be cut, the local three-dimensional shape of the nuclear tank is fitted within the bounding box, in which the cutting trajectories and normal vectors are planned accurately. Findings The cutting precision is verified by changing the tool into a dial indicator in the simulation and the experiment process. The octree data structure with omnidirectional pointing vectors can effectively improve the filtering accuracy of the scattered point cloud. The point cloud filter algorithm combined with the structure calibration methods for the integrated measurement and processing system can ensure the final machining accuracy of the robot. Originality/value Aiming at the problems of large measurement noise interference, complex transformations between coordinate systems and difficult accuracy guarantee, this paper proposes structure calibration, point cloud filtering and point cloud-based planning algorithm, which can greatly improve the reliability and accuracy of the system. Simulation and experiment verify the final cutting accuracy of the whole system.</p
SIASAIL-I solar sail: From system design to on-orbit demonstration mission
No full textSolar sails, characterized by passive propulsion, have attracted an increasing attention of researchers, which has important value in the field of deep space exploration. SIASAIL-I is an on-orbit verification mission for solar sails based on a 6U cubesat, designed and produced by Shenyang Institute of Automation, Chinese Academy of Sciences (SIA, CAS). SIASAIL-I deployment mechanism is composed by two-stage deployment system, which can achieve direction transformation, stretching and membrane deploying in the narrow space of the cubesat. SIASAIL-I only takes up within 0.5U (48 mm × 90 mm × 95 mm) space and the total mass is within 1kg. SIASAIL-I was successfully launched from the Xiaoxiang-1 07 satellite on August 31, 2019, and successfully deployed a 0.78 m × 0.78 m sail in low earth orbit in December. This paper introduces the design process of SIASAIL-I system, including deployment mechanism, a series of ground experiments, and an de-orbit mission analysis, which aims to provide some engineering experience for the development of solar sails.</p
A Quality-Related Fault Detection Method Based on the Dynamic Data-Driven Algorithm for Industrial Systems
No full textFor nearly a decade, quality-related fault detection algorithms have been widely used in industrial systems. However, the majority of these detection strategies rely on static assumptions of the operating environment. In this paper, taking the time series of variables into consideration, a dynamic kernel entropy component regression (DKECR) framework is proposed to address the instability of quality-related fault detection due to the existing dynamic characteristics. Compared with the typical kernel entropy component analysis method, the proposed method constructs the relationship between process states and quality states to further interpret the direct effect on the product taken by the fault. In the proposed approach, process measurements are converted to a lower-dimensional subspace with a specific angular structure that is more comprehensive than traditional subspace approaches. In addition, the angular statistics and their relevant thresholds are exploited to enhance the quality-related fault detection performance. Finally, the proposed method will be compared with three methods by means of a numerical example and two industrial scenarios to demonstrate its practicality and effectiveness.</p