Shenyang Institute of Automation,Chinese Academy Of Sciences
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Microstructure characteristics of CFRP deep groove processed by water jet-guided laser processing technology
No full text碳纤维增强塑料(CFRP)中树脂基体和纤维增强相两种异质材料的性能存在巨大差异，使得其在航空航天领域的广泛应用受到现有加工能力的制约。因此，具有热损伤小、加工深度能力强等优势的水导激光加工技术在特种加工领域展现出优越的加工能力。本文基于有限元法中的单元生死技术，建立了水导激光加工非均质纤维树脂基体的三维瞬态温度场模型。在该模型下，利用双向循环扫描的加工方式对切面的微观形貌进行仿真与实验研究。研究表明：水导激光加工时水射流对材料的强对流换热效果显著，使材料的去除率和排屑率保持在一个较高的水平。在深槽加工时，铺层为90°的表层碳纤维会出现断裂现象，这成为断面损伤的主要来源。通过对切面不同侧边、不同深度的表面形貌进行分析，认为水射流高效的排屑率是实现水导激光高精度加工的关键因素。因此，改变扫描深宽比能有效减少深槽处的纤维损伤，切面可以获得较小的粗糙度和锥度。当扫描深宽比减少一倍时，损伤区域缩小46%。</p
Electric and Heating Combined System Dispatch Based on Multi-Objective and Two-stage Stochastic Programming Method
No full text我国北方地区的可再生能源以风能为主，风力发电是改善北方地区能源利用结构的重要方式。但风电出力具有很强的随机性和波动性，无法进行精准预测，从而对电热综合能源系统调度优化带来了困难。本文提出了以成本最小化和弃风最小化为目标的一种多目标两阶段随机规划方法（Multi-objective and Two-stage Stochastic Programming， MOTSP），其中采用两阶段的随机规划模型对成本最小化部分进行建模分析，第一阶段以火电机组的启停成本为调度目标，第二阶段以机组运行成本为调度目标。最后采用多目标算法NSGA-Ⅱ中对解的筛选机制求解随机规划问题。该方法利用高斯分布描述负荷和风力发电预测误差来解决风电出力的不确定性，采用蒙特卡罗方法生成随机场景，并采用反向缩减技术对场景进行削减。仿真结果表明，本文提出的MOTSP算法比其他多种智能算法的解集更均匀广泛，收敛性更好，能够最大限度地减少弃风并使机组运营成本最小。</p
Combining micropipette and atomic force microscopy for single-cell drug delivery and simultaneous cell mechanics measurement
No full text细胞力学特性与细胞生理病理变化过程及机体健康状态密切相关， 研究细胞力学特性对于揭示生命活动内在机制具有重要科学意义. 原子力显微镜(AFM)的出现为单细胞研究提供了新的技术手段， 它不仅可以在溶液环境下对单个活细胞的形貌结构进行高分辨率成像， 还能够对细胞力学特性进行定量测量. 基于AFM的单细胞力学特性研究在过去的数十年中取得了巨大的成功， 为细胞生理病理行为带来了大量新的认识， 已成为生命科学领域的重要研究方法. 然而， 由于AFM探针自身难以进行药物递送， 目前在超微量药物刺激下的AFM细胞力学特性实时探测方面仍然面临巨大挑战. 本文通过将微针与AFM结合， 发展了可对单细胞进行精准药物激励及力学特性同步测量的方法. 首先搭建了基于微针的细胞显微注射系统， 分析了微针针尖孔径尺寸对细胞注射的影响， 并实现了对单个活细胞的荧光染液有效递送. 在此基础上， 结合微针注射和AFM压痕技术， 建立了超微量化学刺激前后细胞力学特性测量过程. 最后， 利用所建立的方法研究了化疗药物刺激下的单细胞力学特性实时变化. 研究结果为精准激励下的单细胞力学特性同步探测提供了新的方法和思路， 对于生命科学研究具有潜在积极意义.</p
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 proposed SiamOAN outperforms many state-of-the-art trackers and runs at approximately 67 fps on GPU RTX3090.</p
Research on water jet-guided laser micro-hole machining of 6061 aluminum alloy
No full textWith the rapid development of the information age, electronic components are developing toward miniaturization, which makes the manufacturing of chips more and more difficult. Water jet-guided laser processing technology (WJGL) is a composite processing technology that combines pulsed laser and water jet, which can ensure the accuracy and efficiency of processing while small size parts machining. This paper is based on the "element birth and death" technique in the finite element method and the three-dimensional transient temperature field and subsequent material removal model of 6061 aluminum alloy are established. The effects of laser average power, pulse repetition frequency, and pulse action time on the transient thermal distribution, aperture, taper, and other forming qualities with the two technologies of fixed-point drilling and spiral drilling, respectively, are studied. Combining the experimental process, the general rule of morphology change of the micro-hole is obtained. The results show that WJGL of micro-hole is based on the combined effect of thermal ablation and real-time cooling. Spiral drilling can maintain a better hole shape but fixed-point drilling can achieve a smaller hole taper. With the increase of laser power, the hole taper increases, reaching saturation at 8 W. The repetition frequency is between 50 and 70 kHz to obtain better hole morphology while maintaining better processing efficiency, and the minimum hole taper is 8.21°.</p
Is the aspect ratio of cells important in deep learning? A robust comparison of deep learning methods for multi-scale cytopathology cell image classification: From convolutional neural networks to visual transformers
No full textCervical cancer is a very common and fatal type of cancer in women. Cytopathology images are often used to screen for this cancer. Given that there is a possibility that many errors can occur during manual screening, a computer-aided diagnosis system based on deep learning has been developed. Deep learning methods require a fixed dimension of input images, but the dimensions of clinical medical images are inconsistent. The aspect ratios of the images suffer while resizing them directly. Clinically, the aspect ratios of cells inside cytopathological images provide important information for doctors to diagnose cancer. Therefore, it is difficult to resize directly. However, many existing studies have resized the images directly and have obtained highly robust classification results. To determine a reasonable interpretation, we have conducted a series of comparative experiments. First, the raw data of the SIPaKMeD dataset are pre-processed to obtain standard and scaled datasets. Then, the datasets are resized to 224 × 224 pixels. Finally, 22 deep learning models are used to classify the standard and scaled datasets. The results of the study indicate that deep learning models are robust to changes in the aspect ratio of cells in cervical cytopathological images. This conclusion is also validated via the Herlev dataset.</p
Incremental Learning Framework for Autonomous Robots based on Q-learning and the Adaptive Kernel Linear Model
No full textThe performance of autonomous robots in varying environments needs to be improved. For such incremental improvement, here we propose an incremental learning framework based on Q-learning and the adaptive kernel linear (AKL) model. The AKL model is used for storing behavioral policies that are learned by Q-learning. Both the structure and parameters of the AKL model can be trained using a novel L2-norm kernel recursive least squares (L2-KRLS) algorithm. AKL model initially without nodes and gradually accumulates content. The proposed framework allows to learn new behaviors without forgetting the previous ones. A novel local -greedy policy is proposed to speed the convergence rate of Q-learning. It calculates the exploration probability of each state for generating and selecting more important training samples. The performance of our incremental learning framework was validated in two experiments. A curve fitting example shows that the L2-KRLS based AKL model is suitable for incremental learning. The second experiment is based on robot learning tasks. The results show that our framework can incrementally learn behaviors in varying environments. Local -greedy policy-based Q-learning is faster than existing Q-learning algorithms.</p
Monocular Visual-Inertial and Robotic-Arm Calibration in a Unifying Framework
No full textReliable and accurate calibration for camera, inertial measurement unit (IMU) and robot is a critical prerequisite for visual-inertial based robot pose estimation and surrounding environment perception. However, traditional calibrations suffer inaccuracy and inconsistency. To address these problems, this paper proposes a monocular visual-inertial and robotic-arm calibration in a unifying framework. In our method, the spatial relationship is geometrically correlated between the sensing units and robotic arm. The decoupled estimations on rotation and translation could reduce the coupled errors during the optimization. Additionally, the robotic calibration moving trajectory has been designed in a spiral pattern that enables full excitations on 6 DOF motions repeatably and consistently. The calibration has been evaluated on our developed platform. In the experiments, the calibration achieves the accuracy with rotation and translation RMSEs less than 0.7 degrees and 0.01 m, respectively. The comparisons with state-of-the-art results prove our calibration consistency, accuracy and effectiveness.</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
A Comprehensive Review of Markov Random Field and Conditional Random Field Approaches in Pathology Image Analysis
No full textPathology image analysis is an essential procedure for clinical diagnosis of numerous diseases. To boost the accuracy and objectivity of the diagnosis, nowadays, an increasing number of intelligent systems are proposed. Among these methods, random field models play an indispensable role in improving the investigation performance. In this review, we present a comprehensive overview of pathology image analysis based on the Markov Random Fields (MRFs) and Conditional Random Fields (CRFs), which are two popular random field models. First of all, we introduce the framework of two random field models along with pathology images. Secondly, we summarize their analytical operation principle and optimization methods. Then, a thorough review of the recent articles based on MRFs and CRFs in the field of pathology is presented. Finally, we investigate the most commonly used methodologies from the related works and discuss the method migration in computer vision.</p