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Image Segmentation Based on An Improved Pulse Coupled Neural Network for Atomic Force Microscopy

Image Segmentation Based on An Improved Pulse Coupled Neural...

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2022 IEEE International Conference on Real-Time Computing and robotics, RCAR 2022

作者： Chang, Yingao Wu, Yinan Fang, Yongchun Fan, Zhi Nankai University Inst. of Robotics and Automat. Info. System and the Tianjin Key Laboratory of Intelligent Robotics College of Artificial Intelligence Tianjin300350 China

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

In this study, a novel method combining pulse coupled neural network (PCNN) and social network search (SNS) is proposed to achieve accurate image segmentation for an atomic force microscopy (AFM). The proposed method utilizes the biological visual characteristics of PCNN and the solution space search ability of SNS to determine the optimal key parameters, which can address the issue of incorrect image segmentation caused by different topographic heights of specimens in an AFM image. In the tests, the performance of the proposed method is compared with the traditional PCNN method and the Otsu method, which demonstrates that the proposed method can automat.cally segment the AFM image with higher accuracy and robustness. © 2022 IEEE.

关键词： Image segmentation

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A Coupling Enhanced Emergency Braking Approach for Double Pendulum Cranes

A Coupling Enhanced Emergency Braking Approach for Double Pe...

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2022 IEEE International Conference on Real-Time Computing and robotics, RCAR 2022

作者： Chen, He Li, Mengyuan Wu, Yinan Hebei University of Technology School of Artificial Intelligence Tianjin300401 China Nankai University Inst. of Robotics and Automat. Info. System and the Tianjin Key Laboratory of Intelligent Robotics College of Artificial Intelligence Tianjin300350 China

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

Crane systems are commonly used transportation tools in industry to transport heavy cargoes to corresponding target positions. If the cargo shape is large which could not be regarded as a mass point or the hook mass also needs to be taken into account, the cranes would perform as double pendulum systems, whose dynamics are more complex than those of single pendulum systems. Since the working environment for crane systems can be very complex in practice, unavoidable emergency situations may occur, which are very dangerous. To avoid possible accidents, effective emergency braking methods are urgently needed, especially for double pendulum cranes with large cargoes. Considering this fact, this paper presents a coupling enhanced braking strategy for double pendulum cranes, which stops the trolley fast and achieves double pendulum vibration elimination objectives simultaneously. Specifically, an elaborately designed variable consisting of the trolley movement, the hook's swing, and the payload's swing, is utilized as feedback, which enhances the system couplings and improves the vibration elimination ability. Some constraint items are also introduced to restrict the payload in a safety domain and further avoid possible collisions. The effectiveness is rigorously analyzed and proved by mathematical analysis. Some experiments are executed to show the performance of the designed strategy. © 2022 IEEE.

关键词： Cranes

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Celebrating Robustness in Efficient Off-Policy Meta-Reinfo.cement Learning

Celebrating Robustness in Efficient Off-Policy Meta-Reinforc...

引用

2022 IEEE International Conference on Real-Time Computing and robotics, RCAR 2022

作者： Liu, Ziyi Li, Zongyuan Cao, Qianqian Wan, Yuan Guo, Xian NanKai University Inst. of Robotics and Automat. Info. System and the Tianjin Key Laboratory of Intelligent Robotics College of Artificial Intelligence Tianjin300350 China Wuhan University of Technology Department of Mathematical WuHan430070 China

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

Deep reinfo.cement learning algorithms can enable agents to learn policies for complex tasks without expert knowledge. However, the learned policies are typically specialized to one specific task and can not generalize to new tasks. While meta-reinfo.cement learning (meta-RL) algorithms can enable agents to solve new tasks based on prior experience, most of them build on on-policy reinfo.cement learning algorithms which require large amounts of samples during meta-Training and do not consider task-specific features across different tasks and thus make it very difficult to train an agent with high performance. To address these challenges, in this paper, we propose an off-policy meta-RL algorithm abbreviated as CRL (Celebrating Robustness Learning) that disentangles task-specific policy parameters by an adapter network to shared low-level parameters, learns a probabilistic latent space to extract universal info.mation across different tasks and perform temporal-extended exploration. Our approach outperforms baseline methods both in sample efficiency and asymptotic performance on several meta-RL benchmarks. © 2022 IEEE.

关键词： Reinfo.cement learning

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Intercellular Movement Tracking for Damage Assessment during Cell Micromanipulation 11

Intercellular Movement Tracking for Damage Assessment during...

引用

11th IEEE Annual International Conference on CYBER Technology in automat.on, Control, and intelligent systems, CYBER 2021

作者： Chen, Yizhe Du, Yue Liu, Yaowei Zhao, Qili Sun, Mingzhu Cui, Maosheng Zhao, Xin Inst. of Robotics and Automat. Info. System and the Tianjin Key Laboratory of Intelligent Robotic Nankai University Tianjin300350 China Institute of Animal Science and Veterinary of Tianjin Tianjin300381 China

ISBN: (纸本)9781665425278

robotic cell micromanipulation that is designed for delicate operation has brought new prospects for enhancing cell developmental potential. However, mechanical damage of cells during robotic micromanipulation cannot be ignored such that it compromises the effort of the system in improving cell viability. Therefore, cell damage assessment is particularly important, but also a great challenge due to the complexity of the composition of a cell and its functions. In this paper, a novel cell damage assessment method was proposed, relying on the sensing of mitochondrial micron-scale displacement and intracellular strain. Mitochondria in living cells were stained and used as physical markers to avoid the shortcomings of introducing physical markers into cell body. We designed a quantitative marker to characterize cell mechanical damage during the penetration process. The experimental result shows that the high-strain damage is intensified as the penetration proceeds. The maximum displacement and mean displacement were respectively 3.87 pixels (2.42\mu m) and 0.78 pixels (0.49\mu m). The maximum strain and mean strain were respectively 0.107 and 0.0143 between two adjacent frames with the micropipette speed of 10\mu m/s. The maximum displacement measurement error was from 0.294 before optimization to 0.065 after optimization. The quantitative damage marker was sufficiently sensitive to sense the different levels of damage at different time sequences. At the penetration proceeds, the value of such marker at the high-strain region was several times higher than that of the low-strain region. The proposed method has implication for improving the biological efficacy of the robotic micromanipulation. © 2021 IEEE.

关键词： robotics

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Visual Detection of Cells in Brain Tissue Slice for Patch Clamp system 11

Visual Detection of Cells in Brain Tissue Slice for Patch Cl...

引用

11th IEEE Annual International Conference on CYBER Technology in automat.on, Control, and intelligent systems, CYBER 2021

作者： Ma, Yuxin Cai, Yunyao Wang, Zeyu Sun, Mingzhu Zhao, Xin College of Artificial Intelligence Nankai University Tianjin300071 China Inst. of Robotics and Automat. Info. System and the Tianjin Key Laboratory of Intelligent Robotic Nankai University Tianjin300350 China

ISBN: (纸本)9781665425278

Whole-cell patch clamping is the gold-standard technology for electrical activities recordings of living single cells. Since it is difficult to train patch clamp operators, automat.c patch clamp technologies have gained much attention. The key step in fully automat.d patch clamping is visual detection of cells in brain tissue slice under view of differential interference contrast (DIC) microscope. This paper proposes a cell detection method based on DIC images, which includes image classification based on image clarity and visual detection of cells in brain tissue slice based on machine learning. Firstly, combining the gray features and texture features of DIC images, we define a clarity evaluation function based on principal component analysis (PCA). Afterwards, by using K-Means Algorithm, DIC images can be divided into clear class and blurred class. The precision of this method is 99.1% in test set, which indicates that it can select the images with clear cell contour from a series of DIC images for subsequent processing. Secondly, we design a neuron detection process based on sliding window and pre-Trained classifier to take cell detection in the selected clear images. We finally choose GoogLeNet as classifier in the detection process after comparing the advantages and disadvantages of classification methods based on support vector machine (SVM) and deep learning. The results show that the detected cells are basically operable living cells. The proposed cell detection technology in this paper provides an effective visual tool for fully automat.d patch clamping. © 2021 IEEE.

关键词： Image classification

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