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A fast sampling based evolutionary algorithm for million-dimensional multiobjective optimization

SWARM AND EVOLUTIONARY COMPUTATION 2022年第0期75卷

作者： Li, Lianghao He, Cheng Cheng, Ran Li, Hongbin Pan, Linqiang Jin, Yaochu Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automation Key Lab Image Informat Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Elect & Elect Engn Wuhan 430074 Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China Bielefeld Univ Fac Technol D-33619 Bielefeld Germany

With their complexity and vast search space, large-scale multiobjective optimization problems (LSMOPs) chal-lenge existing multiobjective evolutionary algorithms (MOEAs). Recently, several large-scale multiobjective evolutionary algorithms have been developed to tackle LSMOPs. Unlike conventional MOEAs that concentrate on selection operations in the objective space, large-scale MOEAs emphasize operations in the decision space, such as offspring generation, to tackle the large number of decision variables. Nevertheless, most present large-scale MOEAs experience difficulty effectively and efficiently solving LSMOPs with tens of thousands or more decision variables or exhibit poor versatility in solving different LSMOPs. We propose a fast large-scale MOEA framework with reference-guided offspring generation, named FLEA, aiming at these issues. Generally, FLEA constructs several reference vectors in the decision space to steer the sampling of promising solutions during offspring generation. A parameter is used to allocate computation resources between the convergence and diversity of the offspring population adaptively. Without computationally expensive problem reformulation or decision variable analysis techniques, the proposed method can significantly accelerate the search speed of conventional MOEAs in solving LSMOPs. FLEA is examined on various LSMOPs with up to 1.6 million decision variables, demonstrating its superior effectiveness, efficiency, and versatility in large-scale multiobjective optimization.

关键词： Evolutionary algorithm Multiobjective optimization Offspring generation Large-scale optimization Reference vector

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Improving magnetic nanothermometry accuracy through mixing-frequency excitation

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REVIEW OF SCIENTIFIC INSTRUMENTS 2021年第2期92卷 024901-024901页

作者： Guo, Silin Liu, Jay Du, Zhongzhou Liu, Wenzhong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Ningbo Chuanshanjia Elect & Mech Co Ltd Ningbo 315400 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

This study proposes a temperature model for the relaxation of magnetic nanoparticles and a phase measurement method under a mixing-frequency excitation field, which can improve the accuracy of temperature measurements in magnetic nanothermometry. According to the Debye-based magnetization model for magnetic nanoparticles, phases at mixing frequencies are used to solve the problem of a delay in the relaxation phase of the magnetic field at a high frequency. This method can improve the signal-to-noise ratio of the response of the magnetic nanoparticles and weaken the phase shift of the detection coils caused by the changes in temperature. The results of experiments show that the proposed method can achieve static temperature measurement error less than 0.1 K and dynamic temperature measurement error less than 0.2 K.

关键词： Temperature measurement

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A 3D-Printed Fin Ray Effect Inspired Soft Robotic Gripper with Force Feedback

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MICROMACHINES 2021年第10期12卷 1141-1141页

作者： Yang, Yang Jin, Kaixiang Zhu, Honghui Song, Gongfei Lu, Haojian Kang, Long Nanjing Univ Informat Sci & Technol Sch Automat Nanjing 210044 Peoples R China East China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China Zhejiang Univ State Key Lab Ind Control Technol Inst Cyber Syst & Control Hangzhou 310027 Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Key Lab Intelligent Percept & Syst High Dimens In PCA LabMinist Educ Nanjing 210094 Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Jiangsu Key Lab Image & Video Understanding Socia Nanjing 210094 Peoples R China

Soft robotic grippers are able to carry out many tasks that traditional rigid-bodied grippers cannot perform but often have many limitations in terms of control and feedback. In this study, a Fin Ray effect inspired soft robotic gripper is proposed with its whole body directly 3D printed using soft material without the need of assembly. As a result, the soft gripper has a light weight, simple structure, is enabled with high compliance and conformability, and is able to grasp objects with arbitrary geometry. A force sensor is embedded in the inner side of the gripper, which allows the contact force required to grip the object to be measured in order to guarantee successful grasping and to provide the most suitable gripping force. In addition, it enables control and data monitoring of the gripper's operating state at all times. Characterization and grasping demonstration of the gripper are given in the Experiment section. Results show that the gripper can be used in a wide range of scenarios and applications, such as the service robot and food industry.

关键词： soft robotic gripper force feedback 3D printing compliant structure Fin Ray effect

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Context Prior for Scene Segmentation

Context Prior for Scene Segmentation

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yu, Changqian Wang, Jingbo Gao, Changxin Yu, Gang Shen, Chunhua Sang, Nong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan Peoples R China Univ Adelaide Adelaide SA Australia Chinese Univ Hong Kong Hong Kong Peoples R China Tencent Shenzhen Peoples R China

ISBN: (纸本)9781728171685

Recent works have widely explored the contextual dependencies to achieve more accurate segmentation results. However, most approaches rarely distinguish different types of contextual dependencies, which may pollute the scene understanding. In this work, we directly supervise the feature aggregation to distinguish the intra-class and interclass context clearly. Specifically, we develop a Context Prior with the supervision of the Affinity Loss. Given an input image and corresponding ground truth, Affinity Loss constructs an ideal affinity map to supervise the learning of Context Prior. The learned Context Prior extracts the pixels belonging to the same category, while the reversed prior focuses on the pixels of different classes. Embedded into a conventional deep CNN, the proposed Context Prior Layer can selectively capture the intra-class and inter-class contextual dependencies, leading to robust feature representation. To validate the effectiveness, we design an effective Context Prior Network (CPNet). Extensive quantitative and qualitative evaluations demonstrate that the proposed model performs favorably against state-of-the-art semantic segmentation approaches. More specifically, our algorithm achieves 46.3% mIoU on ADE20K, 53.9% mIoU on PASCAL-Context, and 81.3% mIoU on Cityscapes. Code is available at https://***/ContextPrior.

关键词： Pixels

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Fast Semisupervised Classification Using Histogram-Based Density Estimation for Large-Scale Polarimetric SAR Data

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2019年第12期16卷 1844-1848页

作者： Liu, Hongying Wang, Feixiang Yang, Shuyuan Hou, Biao Jiao, Licheng Yang, Ri Xidian Univ Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Shaanxi Peoples R China Xian Satellite Control Ctr Xian 710043 Shaanxi Peoples R China

In order to obtain high classification accuracy and reduce time consumption for large-scale polarimetric synthetic-aperture radar (PolSAR) data. In this letter, we propose a fast semisupervised classification algorithm using histogram-based density estimation (called FSHDE). First, a noniterative collaborative training using our proposed Wishart-clustering selection strategy is designed to expand the labeled sample set from unlabeled samples. Second, a fast feature mapping based on histogram density estimation is employed to reliably capture the interaction of nonlinear features. Third, submodular optimization is used to select optimal subspace features to reduce feature correlation. Experimental results on synthetic and real PolSAR data indicate that FSHDE greatly reduces the time consumption and improves the accuracy for terrain classification compared with the state-of-the-art methods.

关键词： Co-training feature extraction polarimetric synthetic-aperture radar (PolSAR) semisupervised learning

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Scaling properties of scale-free networks in degree-thresholding renormalization flows

arXiv

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arXiv 2021年

作者： Chen, Dan Cai, Defu Su, Housheng The School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Huazhong University of Science and Technology Wuhan430074 China The State Grid Hubei Electric Power Research Institute Wuhan China

We study the statistical properties of observables of scale-free networks in the degree-thresholding renormalization (DTR) flows. For BA scale-free networks with different sizes, we find that their structural and dynamical observables have similar scaling behavior in the DTR flow. The finite-size scaling analysis confirms this view and reveals a scaling function with a single scaling exponent that collectively captures the changes of these observables. Furthermore, for the scale-free network with a single initial size, we use its DTR snapshots as the original networks in the DTR flows, then perform a similar finite-size scaling analysis. Interestingly, the initial network and its snapshots share the same scaling exponent as the BA synthetic network. Our findings have important guiding significance for analyzing the structure and dynamic behavior of large-scale networks. Such as, in large-scale simulation scenarios with high time complexity, the DTR snapshot could serve as a substitute or guide for the initial network and then quickly explore the scaling behavior of initial networks. © 2021, CC BY.

关键词： Scaling laws

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Performing DNA Strand Displacement with DNA Polymerase 14th

Performing DNA Strand Displacement with DNA Polymerase

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14th International Conference on Bio-inspired Computing: Theories and Applications, BIC-TA 2019

作者： Wang, Zhiyu Hu, Yingxin Chen, Zhekun Liao, Sulin Huang, Yabing Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan730074 China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan730074 China Department of Pathology Renmin Hospital of Wuhan University Wuhan430060 China

ISBN: (纸本)9789811534140

Significant improvements to the dynamic DNA nano science have emerged in recent years, primarily due to the elaborate design of toehold-mediated DNA strand displacement (TMSD). However, it remains an ongoing challenge to design huge-scaled TMSD based sophisticated dynamic DNA structures without base-pair mismatching or unwanted secondary structures, because the sequence design complexity of TMSD will dramatically increase as the scale increases. Here we report a new polymerase triggered strand displacement (PTSD) mechanism to realize DNA strand displacement with the potential of constructing complicated dynamic DNA nanostructures. By employing proper polymerase with strand displacement activity, the target strand in a duplex will be displaced and freed during the polymerization. In this manner, DNA strand displacement takes place without considering the design complexity of branch migration domain. The mechanism was successfully applied in constructing one-layer and two-layer circuits in which the parameters involved were explored, suggesting that PTSD is feasible and able to be harnessed to construct cascading DNA circuits with high complexity. © 2020, Springer Nature Singapore Pte Ltd.

关键词： DNA

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One-step deconvolution for multi-angle TIRF microscopy with enhanced resolution

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BIOMEDICAL OPTICS EXPRESS 2019年第3期10卷 1097-1110页

作者： Fan, Junchao Huang, Xiaoshuai Li, Liuju Chen, Liangyi Tan, Shan Huazhong Univ Sci & Technol Sch Automat Minist Educ China Key Lab Image Proc & Intelligent Control Wuhan 430074 Hubei Peoples R China Peking Univ Inst Mol Med Beijing Key Lab Cardiometab Mol Med State Key Lab Membrane Biol Beijing 100871 Peoples R China

Total internal reflection fluorescence microscopy (TIRF microscopy) uses a rapid decay of evanescent waves to excite fluorophores within several hundred nanometers (nm) beneath the plasma membrane, which can effectively suppress excitation of fluorescence signals in the deep layers. From image stacks obtained with a plurality of different incident angles, a three-dimensional spatial structure of the observed sample can be reconstructed by a Multi-Angle-TIRF (MA-TIRF) algorithm that provides an axial resolution of similar to 50 nm. Taking into account the point spread function (PSF) of the TIRF microscopes, we further increase its lateral resolution by introducing a fast deconvolution algorithm into the reconstruction of MA-TIRF data (DMA-TIRF), which is approached in just one step of minimizing the reconstruction function. We also introduce a TV regularization term in the deconvolution algorithm to suppress artifacts induced by the excessive noise. Therefore, based on the hardware of existing MA-TIRF microscopes, the proposed DMA-TIRF algorithm has achieved lateral and axial resolutions of similar to 200 and similar to 50 nm, respectively. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

关键词： Confocal microscopy Diffraction limit Evanescent waves Fluorescence microscopy image stacking Total internal reflection

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The Computational Power of Cell-like P Systems with Symport/Antiport Rules and Promoters

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FUNDAMENTA INFORMATICAE 2019年第2-3期164卷 207-225页

作者： Jiang, Suxia Wang, Yanfeng Xu, Jinbang Xu, Fei Zhengzhou Univ Light Ind Henan Key Lab Informat Based Elect Appliances Zhengzhou 450002 Henan Peoples R China Huazhong Univ Sci & Technol Sch Automat Key Lab Image Informat Proc & Intelligent Control Wuhan 430074 Hubei Peoples R China

Cell-like P systems with symport/antiport rules (CSA P systems, for short) are a class of computational models in membrane computing, inspired by the way of transmembrane transport of substances through membrane channels between neighboring regions in a cell. In this work, we propose a variant of CSA P systems, called cell-like P systems with symport/antiport rules and promoters (CSAp P systems, for short), where symport/antiport rules are regulated by multisets of promoters. The computational power of CSAp P systems is investigated. Specifically, it is proved that CSAp P systems working in the maximally parallel mode, having arbitrary large number of membranes and promoters and using only symport rules of length 1 or antiport rules of length 2, are able to compute only finite sets of non-negative integers. Furthermore, we show that CSAp P systems with two membranes working in a sequential mode when having at most two promoters and using only symport rules of length 2, or having at most one promoter and using symport rules of length 1 and antiport rules of length 2, are Turing universal.

关键词： Bio-inspired computing Membrane computing Cell-like P system Symport/antiport rule Promoter Universality

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RTM3D: Real-Time Monocular 3D Detection from Object keypoints for Autonomous Driving 16th

RTM3D: Real-Time Monocular 3D Detection from Object Keypoin...

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16th European Conference on Computer Vision, ECCV 2020

作者： Li, Peixuan Zhao, Huaici Liu, Pengfei Cao, Feidao Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang China Key Lab of Image Understanding and Computer Vision ShenyangLiaoning China

ISBN: (纸本)9783030585792

In this work, we propose an efficient and accurate monocular 3D detection framework in single shot. Most successful 3D detectors take the projection constraint from the 3D bounding box to the 2D box as an important component. Four edges of a 2D box provide only four constraints and the performance deteriorates dramatically with the small error of the 2D detector. Different from these approaches, our method predicts the nine perspective keypoints of a 3D bounding box in image space, and then utilize the geometric relationship of 3D and 2D perspectives to recover the dimension, location, and orientation in 3D space. In this method, the properties of the object can be predicted stably even when the estimation of keypoints is very noisy, which enables us to obtain fast detection speed with a small architecture. Training our method only uses the 3D properties of the object without any extra annotations, category-specific 3D shape priors, or depth maps. Our method is the first real-time system (FPS > 24) for monocular image 3D detection while achieves state-of-the-art performance on the KITTI benchmark. © 2020, Springer Nature Switzerland AG.

关键词： Real time systems

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