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arXiv 2022年

作者： Zhang, Zihan Xiang, Xiang Peng, Xuehua Shao, Jianbo MoE Key Lab of Image Info Processing & Intelligent Control School of Artificial Intelligence & Automation China Wuhan Children's Hospital Tongji Medical College China Huazhong University of Science and Technology Wuhan430074 China

Neuroblastoma is one of the most common cancers in infants, and the initial diagnosis of this disease is difficult. At present, the MYCN gene amplification (MNA) status is detected by invasive pathological examination of tumor samples. This is time-consuming and may have a hidden impact on children. To handle this problem, we adopt multiple machine learning (ML) algorithms to predict the presence or absence of MYCN gene amplification. The dataset is composed of retrospective CT images of 23 neuroblastoma patients. Different from previous work, we develop the algorithm without manually-segmented primary tumors which is time-consuming and not practical. Instead, we only need the coordinate of the center point and the number of tumor slices given by a subspecialty-trained pediatric radiologist. Specifically, CNN-based method uses pre-trained convolutional neural network, and radiomics-based method extracts radiomics features. Our results show that CNN-based method outperforms the radiomics-based method. Copyright © 2022, The Authors. All rights reserved.

关键词： Computerized tomography

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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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Manifold Learning Inspired Mating Restriction for Evolutionary Constrained Multiobjective Optimization 11th

Manifold Learning Inspired Mating Restriction for Evolutiona...

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11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021

作者： Li, Lianghao He, Cheng Cheng, Ran Pan, Linqiang 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 Wuhan430074 China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9783030720612

Mating restriction strategies are capable of restricting the distribution of parent solutions for effective offspring generation in evolutionary algorithms (EAs). Studies have shown the importance of these strategies in improving the performance of EAs for multiobjective optimization. Our previous study proposed a specific manifold learning inspired mating restriction (MLMR) strategy. It has shown promising capability of solving multiobjective optimization problems (MOPs) with complicated Pareto set shapes. However, the effect of mating restriction strategies in solving constrained MOPs is yet to be well studied. Here, we investigate the effectiveness of MLMR for solving constrained MOPs. The MLMR strategy is embedded into some representative multiobjective EAs and tested on various benchmark constrained MOPs. Experimental results indicate the encouraging performance of MLMR in constrained multiobjective optimization. © 2021, Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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Second-Order Consensus of Hybrid Multiagent Systems

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2021年第10期51卷 6503-6512页

作者： Su, Housheng Wang, Xin Chen, Xia Zeng, Zhigang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Image Proc & Intelligent Control Key Lab Educ Minist China Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Adv Electromagnet Engn & Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Elect & Elect Engn Wuhan 430074 Peoples R China

This article studies the consensus of hybrid second-order multiagent systems (MASs), where the hybrid MAS is constituted by the continuous-time second-order (CTSO) and discrete-time second-order (DTSO) dynamic agents. First, a hybrid consensus algorithm is proposed, where a sample-data control is considered in the CTSO subsystems. Under the proposed hybrid consensus algorithm, the convergence of the hybrid second-order MAS matrix is analyzed. Second, a sufficient and necessary condition is proposed, which indicates that the controller parameters, such as coupling gains and the sampling interval, and eigenvalues of network topology have a significant impact on the system consensus. Finally, several simulation examples are presented to prove the validity of the results.

关键词： Eigenvalues and eigenfunctions Consensus algorithm Multi-agent systems Graph theory Couplings Delays Laplace equations Consensus continuous-time discrete-time hybrid multiagent systems (MASs) second-order

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Spiking neural P systems with target indications

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THEORETICAL COMPUTER SCIENCE 2021年 862卷 250-261页

作者： Wu, Tingfang Zhang, Luping Pan, Linqiang Soochow Univ Sch Comp Sci & Technol Suzhou 215006 Peoples R China Soochow Univ Prov Key Lab Comp Informat Proc Technol Suzhou 215006 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Inst Artificial IntelligenceMinist China Key Lab Image Informat Proc & Intelligent Control Wuhan 430074 Peoples R China

Spiking neural P systems (SNP systems) are a class of distributed and parallel computation models, which are inspired by the way in which neurons process information by means of spikes, where a neuron fires and distributes the spikes to all the neurons linked by synapses with the firing neuron. In this work, a new spike distribution mechanism is introduced, where the produced spikes are distributed to a set of neurons indicated by the target indications. The computation power of SNP systems with this new spike distribution mechanism is investigated. It is demonstrated that SNP systems with such a distribution mechanism are Turing universal as both number generators and function computing devices. Moreover, it is shown that 6 neurons (respectively, 15 neurons) are sufficient for constructing a universal SNP system with the proposed spike distribution mechanism as a number generator (respectively, as a function computing device). By comparing with the classical one, it can be found that the proposed spike distribution mechanism is a powerful feature in terms of the number of neurons used to construct universal SNP systems. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Membrane computing Neural computation Spiking neural P system Computation power Universality

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A Comparison of Large-Scale MOEAs with Informed Initialization for Voltage Transformer Ratio Error Estimation 1

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17th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2022

作者： Li, Lianghao He, Cheng Li, Hongbin 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 Wuhan430074 China School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan430074 China

ISBN: (数字)9789819915491

ISBN: (纸本)9789819915484

Large-scale multiobjective optimization problems (LSMOPs) exist widely in real-world applications. The large number of decision variables in LSMOP leads to a tremendous high-dimensional search space, which is still challenging for existing multiobjective evolutionary algorithms (MOEAs). The voltage transformer ratio error estimation (TREE) problem is a typical LSMOP in real-world applications. A number of large-scale multiobjective evolutionary algorithms (LSMOEAs) have been adopted for solving the TREE problem, but so far there is still considerable potential for improvement in their results. In this paper, we propose a informed initialization for solving the TREE problem. The physical properties of the TREE problem are used to construct an initial set of solutions with specific characteristics. The proposed initialization method is tested against the default initialization method on five TREE problems by being applied on six state-of-the-art LSMOEAs and a classical MOEA. The experimental results demonstrate that the initialization method has a significant effect on the performance of LSMOEAs in solving the TREE problems. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Multiobjective optimization

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Cooperative Target-Surrounding control of Unmanned Surface Vessels Based on MADDPG

Cooperative Target-Surrounding Control of Unmanned Surface V...

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Annual Conference of Industrial Electronics Society

作者： Taoman Li Fei Zhu Zihan Gan Zexing Zhou Xiaotao Wang Renzhi Lu Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China China Ship Scientific Research Center Wuxi China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan China

This article proposes a multi-agent deep reinforce-ment learning algorithm to control a fleet of unmanned surface vessels (USVs) that encircle and capture sea targets. First, a simulation environment for USVs is established based on a dynamic model; two-dimensional control variables are used to control movements in three directions. Second, the multi-agent deep deterministic policy gradient (MADDPG) algorithm is employed to achieve intelligent control of the USVs, using a reward function based on certain prior knowledge. Finally, centralized training and decentralized execution are used to complete the offline learning of multiple agents, and continuous action decisions are made based on the observations of the USV sensors. Simulations demonstrate that the method can capture stationary moving sea targets with any number of multi-agents under disturbances, and exhibits strong robustness and practicality.

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Dispatch of highly renewable energy power system considering its utilization via a data-driven Bayesian assisted optimization algorithm

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KNOWLEDGE-BASED SYSTEMS 2023年 281卷

作者： Yu, Chaofan Li, Yuanzheng Liu, Yun Ge, Leijiao Wang, Hao Luo, Yunfeng Pan, Linqiang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Informat Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol China EU Inst Clean & Renewable Energy Wuhan 430074 Peoples R China South China Univ Technol Sch Elect Power Engn Guangzhou 510641 Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Monash Univ Dept Data Sci & Artificial Intelligence Melbourne Vic 3800 Australia

In recent years, renewable energy (RE) has been widely deployed, and the power system with high penetration RE is gradually formed. However, the high proportion of RE may threaten transmission security of power systems, which in turn limits its utilization. What is more, the interaction between RE penetration and power system transmission security has not been comprehensively investigated so far. To this end, we develop a bi-objective stochastic dispatch model to investigate the relationship between RE utilization and transmission security. It aims to solve the optimal power system dispatch (OPSD) problem with high penetration RE, in which the RE curtailment and the capacity margin of transmission lines are considered as two objectives of the dispatch problem and formulated in the probabilistic forms. With this, the proposed model is a complicated bi-objective stochastic optimization problem, which is difficult to be solved for traditional optimization algorithms. Therefore, we propose a data-driven Bayesian assisted optimization (DBAO) algorithm, based on Bayesian evolutionary optimization and estimation of distribution algorithm to improve the searching efficiency for the proposed model. Case studies on a modified Midwestern US power system verify the effectiveness of our proposed dispatch model and the optimization algorithm of DBAO.

关键词： Renewable energy Data-driven optimization algorithm Power system dispatch Bi-objective optimization Uncertainty

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Clustering-Based Genetic Offspring Generation for Solving Multi-Objective Optimization Problems with Intricate Pareto Sets

SSRN

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SSRN 2023年

作者： Li, Lianghao Lin, Jianqing He, Cheng Pan, Linqiang 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 Wuhan430074 China School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan430074 China

Distinct from conventional benchmarks, multiobjective optimization problems (MOPs) encountered in practical domains exhibit complex variable linkages, resulting in intricate Pareto sets (PSs), such as rotated or nonlinear shapes. Simulated binary crossover (SBX), a widely utilized genetic operator for solving MOPs, exhibits a substantial degeneration in performance when applied to MOPs with intricate PSs. The rotation-based SBX (RSBX) incorporates the rotational property into SBX to handle MOPs with linear but rotated PSs. Nevertheless, RSBX may encounter difficulties in solving MOPs with nonlinear PSs. In order to tackle this challenge, we employ a clustering-based mating restriction strategy to address MOPs with intricate PSs, and propose an MOEA called CRSBX. Generally, the clustering-based mating restriction strategy involves partitioning the parent population into approximately linearly distributed clusters. Subsequently, RSBX is applied to each cluster for effective offspring generation. Empirically, we investigate the impact of the clustering algorithm and its associated parameters on CRSBX. Ablation studies are also conducted to examine the efficacy of the clustering-based mating restriction strategy. Additionally, we compare CRSBX with other representative algorithms on benchmark problems and real-world applications with intricate PSs. Comparison results highlight the promising performance of CRSBX in effectively addressing MOPs with intricate PSs. © 2023, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Identification of Prognostic Signature in Esophageal Cancer Based on Network Analysis 15th

Identification of Prognostic Signature in Esophageal Cancer ...

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15th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2020

作者： Ma, Jianfei Huang, Yabing Key Laboratory of Image Information Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Luoyu Road 1037 Wuhan430074 China Department of Pathology Renmin Hospital of Wuhan University Wuhan430060 China

ISBN: (纸本)9789811613531

Esophageal cancer is the 6th most common cancer in the world with a five-year survival among 15 % to 25 % patients. It’s of great significance to identify prognostic signature for precise prediction of patient survival. In this work, we use a network-based approach to identify the disease genes of esophageal cancer. We construct the co-expression networks of normal and cancer samples by interpreting the gene expression profile and further divide the networks into inactivated subnetwork and enhanced subnetwork. Functional enrichment analysis shows that phosphoprotein and acetylation are both enriched in the inactivated genes and enhanced genes. Furthermore, 5 kinesin family members, KIF11, KIF23, KIF18A, KIF18B, and KIF2A, are all found to be significantly enhanced, suggesting that kinesin is crucial in promoting the formation of esophageal cancer. The 11 genes both inactivated and enhanced in the process of tumor development are found to be prognosis-associated and perform well in evaluating the survival of esophageal cancer patients. This study provides us fundamental recognition about the functional dysregulation in esophageal cancer and help to identify the biomarker for the further development of therapeutic targets. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Diseases

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