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Gated PE-NL-MA: A multi -modal attention based network for video understanding

NEUROCOMPUTING 2021年 423卷 148-157页

作者： Xie, Chengyang Wang, Xiaoping Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Minist China Key Lab Image Proc & Intelligent Control Educ Wuhan 430074 Peoples R China

In multi-modal learning tasks such as video understanding, the most important operations are feature extraction, feature enhancement for single modality and feature aggregation between modalities. In this paper, we present two attention based algorithms, the Position-embedding Non-local (PE-NL) Network and the Multi-modal Attention (MA) feature aggregation method. Inspired by Non-local Neural Networks and Transformers, our PE-NL is a self-attention liked feature enhancement operation and it can capture long-range dependencies and model relative positions. The MA aggregation method merges visual and audio modals while reduces feature dimension and the number of parameters without losing too much accuracy. Both of PE-NL and MA blocks can be plugged into many multi-modal learning architectures. Our Gated PE-NL-MA network achieves competitive results on Youtube-8M dataset. (c) 2020 Elsevier B.V. All rights reserved.

关键词： Neural network Multi-modal Attention mechanism Non-local Video understanding

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A Consensus Model to Manage Minority Opinions and Noncooperative Behaviors in Large Group Decision Making With Probabilistic Linguistic Term Sets

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2021年第6期29卷 1667-1681页

作者： Zhou, Jian-Lan Chen, Jin-An Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

The alternative assessment and decision-making are often complicated and involve enormous decision makers (DMs), which leads to a large group decision making (LGDM) problem, and usually requires to reach consensus within a limited time. As a powerful technique in representing linguistic evaluations, the probabilistic linguistic term set is popular to express the opinions of DMs. Under this scenario, DMs who support the same linguistic term are naturally clustered into one subgroup, whereas the distance formulas are inappropriate because whether the DM supports a linguistic term is a dichotomous variable;therefore, Tanimoto coefficient is introduced to calculate the group consensus level. Additionally, an efficient consensus model is proposed to bridge the gap between the subgroup and group opinions. In particular, a weight adjustment function is proposed to process the minority opinions. A novel method including minor and major adjustment manner is proposed to manage the noncooperative behaviors. DMs are allowed to recluster into another subgroup for major adjustment manner;on the contrary, the adjustment direction and quantities are provided for minor adjustment manner. A case study of forest fire emergency decision-making is explored and a simulation is conducted to verify the proposed consensus model in the LGDM problem. The proposed consensus model is suitable for situations where the subgroup and group decision matrices are subject to preference selections and can be applied in other decision problems.

关键词： Linguistics Decision making Probabilistic logic Uncertainty Artificial intelligence Automation Forestry Consensus model minority opinions noncooperative behavior probabilistic linguistic term set (PLTS) Tanimoto coefficient

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Quasi-Synchronization of Delayed Memristive Neural Networks via a Hybrid Impulsive control

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2021年第3期51卷 1954-1965页

作者： Zhou, Yufeng Zhang, Hao Zeng, Zhigang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430070 Peoples R China Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

This paper investigates the quasi-synchronization of delayed memristive neural networks (MNNs) via a novel hybrid impulsive control algorithm which combines time-triggered and event-triggered impulsive control. The relationship between a predesigned non-negative auxiliary function and a given exponentially decreasing threshold function is used to describe the switching. Under this novel controller, sufficient conditions for the quasi-synchronization are derived by the impulsive differential inequality. In addition, by choosing appropriate parameters or initial conditions such that the initial value of the non-negative auxiliary function is less than that of the event-triggered function, the quasi-synchronization can be realized theoretically as long as the event-triggered impulsive intensity is less than 1. This greatly reduces the conservatism of the existing quasi-synchronization results. Furthermore, the event-triggered rules can avoid the Zeno behavior as long as the event-triggered impulsive intensity is less than 1. This hybrid mechanism can reduce the amount of impulsive control and lessen the network communication. Finally, one example is given to illustrate the validness of the obtained results.

关键词： Synchronization Memristors Switches Delay effects Neural networks Linear systems Event-triggered impulsive control memristive neural networks (MNNs) quasi-synchronization time delays

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A MultiObjective Optimization Approach for Integrated Timetabling and Vehicle Scheduling with Uncertainty

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JOURNAL OF ADVANCED TRANSPORTATION 2021年第1期2021卷

作者： Shen, Yindong Xie, Wenliang Li, Jingpeng Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Univ Stirling Div Comp Sci & Math Stirling Scotland

The timetabling problem (TTP) and vehicle scheduling problem (VSP) are two indispensable problems in public transit planning process. They used to be solved in sequence;hence, optimality of resulting solutions is compromised. To get better results, some integrated approaches emerge to solve the TTP and VSP as an integrated problem. In the existing integrated approaches, the passenger comfort on bus and the uncertainty in the real world are rarely considered. To provide better service for passengers and enhance the robustness of the schedule to be compiled, we study the integrated optimization of TTP and VSP with uncertainty. In this paper, a novel multiobjective optimization approach with the objectives of minimizing the passenger travel cost, the vehicle scheduling cost, and the incompatible trip-link cost is proposed. Meanwhile, a multiobjective hybrid algorithm, which is a combination of the self-adjust genetic algorithm (SGA), large neighborhood search (LNS) algorithm, and Pareto separation operator (PSO), is applied to solve the integrated optimization problem. The experimental results show that the approach outperforms existing approaches in terms of service level and robustness.

关键词： Multiobjective optimization

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Pioneer selection for evolutionary multiobjective optimization with discontinuous feasible region

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SWARM AND EVOLUTIONARY COMPUTATION 2021年 65卷 100932-100932页

作者： Li, Lianghao He, Cheng Xu, Wenting 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 Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen 518055 Peoples R China

Constrained multiobjective optimization problems (CMOPs) are widespread in real-world applications. Nevertheless, CMOPs with discontinuous feasible regions are challenging for existing evolutionary algorithms due to the difficulty of passing through the infeasible regions. Moreover, there are only several benchmark test problems specified for promoting the research in complex constrained multiobjective optimization. To address these two issues, we first propose a set of CMOPs with discontinuous feasible regions by introducing constraints into the widely used DTLZ test problems, and then a pioneer selection strategy is designed to handle these complex constrained optimization problems. The general idea of the proposed constraint handling strategy is simple, which selects some individuals in the population as the pioneer population, aiming to obtain some well-converged solutions without considering the constraints. By adjusting the ratio of the pioneer solutions during the evaluation, the quasi-optimal solutions are expected to approximate the Pareto optimal front. To investigate the performance of the proposed strategy, it is embedded in a classic evolutionary algorithm and compared with three state-of-theart constrained multiobjective evolutionary algorithms. Experimental results demonstrate the effectiveness of the proposed strategy and also show that the proposed benchmark problems are challenging for existing approaches.

关键词： Multiobjective optimization Constraint handling Evolutionary algorithm Infeasible region

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Optimal Tracking Performance Analysis of MIMO control Systems Under Multiple Constraints

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第5期52卷 2734-2743页

作者： Cheng, Lingli Yan, Huaicheng Chi, Ming Zhan, Xisheng Zhou, Ge East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China Hubei Normal Univ Coll Mechatron & Control Engn Huangshi 435002 Hubei Peoples R China Huazhong Univ Sci & Technol Coll Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China Shanghai Electromech Engn Inst Shanghai 201109 Peoples R China

This article is concerned with the optimal tracking performance on linear time-invariant (LTI) discrete-time multi-input-multi-output (MIMO) systems based on quantization, channel noise, and encoding-decoding, as well as bandwidth constraints. Meanwhile, power constraint, feedback and feedforward loops constraints of the systems are also taken into account. The tracking performance expression is described by inner-outer factorization, which is obtained by all stabilizing two-degree-of-freedom compensators. The results will illustrate that tracking performance limitation is decided by nonminimum phase zeros and unstable poles of the given plant. Moreover, time delay, encoding-decoding, channel noise, quantization, and bandwidth constraints also impact the performance. The accuracy of the method is verified by simulation examples.

关键词： Quantization (signal) MIMO communication Bandwidth Linear systems Delay effects Decoding Communication channels Bandwidth constraints encoding-decoding noise quantization tracking performance

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Multi-scale Deformable Deblurring Kernel Prediction for Dynamic Scene Deblurring 11th

Multi-scale Deformable Deblurring Kernel Prediction for Dyna...

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11th International Conference on image and Graphics (ICIG)

作者： Zhu, Kai Sang, Nong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan Peoples R China

ISBN: (纸本)9783030873615;9783030873608

Deblurring aims to restore clear images from blurred ones. Recently deep learning are widely used. Previous methods regard deblurring as dense prediction problems and rarely consider the inverse operation of blur. In this paper, we propose a multi-scale deformable deblurring kernel prediction network for dynamic scene deblurring which uses a coarse-to-fine method to predict the per-pixel deformable deblurring kernel and uses the fusion weight to integrate the latent images in different scales. Since the spatially variable blur scatters pixel information to surrounding sub-pixels and leads to the spatially and quantitively uneven distribution of latent pixel information, the per-pixel deformable deblurring kernel can adaptively select the sub-pixels and linearly combine them into the clean pixel for information aggregation. The multi-scale architecture helps the deformable deblurring kernel enlarge the reception field. The residual image is added to convolution result in each scale to supply refined edges when the kernel cannot cover the areas existing latent pixel information. Besides, we add local similarity loss to constrain deformable deblurring kernel's weight and offset which boosts the deblurring performance. Qualitative and quantitative experiments show that our method can produce competitive deblurring performance.

关键词： Dynamic scene deblurring Deformable convolution Dynamic convolution

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Solving Nonlinear Equations Systems with a Two-Phase Root-Finder Based on Niching Differential Evolution 15th

Solving Nonlinear Equations Systems with a Two-Phase Root-Fi...

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

作者： Duan, Lianlang Xu, Fei Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789811613531

Solving nonlinear equation systems (NESs) is a challenging problems in numerical computation. Two goals should be considered for solving NESs. One is to locate as many roots as possible and the other is to improve the solving precision. In this work, in order to achieve these two goals, a two-phase niching differential evolution (TPNDE) is proposed. The innovation of TPNDE is mainly reflected in the following three aspects. First, a probabilistic mutation mechanism is designed to increase the diversity of the population. Second, a novel reinitialization procedure is proposed to detect converged individuals and explore more new regions that may contain global optima. Third, distribution adjustment local search (DALS) is proposed to deal with the isolated solution and enhance the convergence of the algorithm. To evaluate the performance of TPNDE, we test our TPNDE and 5 state-of-the-art algorithms on 20 NESs with diverse features. The experimental results show that the proposed TPNDE algorithm is better than the other methods. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Nonlinear equations

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Cortical Neural P Systems 15th

Cortical Neural P Systems

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

作者： Fu, Zhongzheng Zhang, Luping Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789811613531

P systems are distributed and parallel computing models inspired from living cells. In this work, a variant of spiking neural P systems, cortical neural P systems, are proposed in the frame work of P systems combining the topology structure of basic P system and information processing style in spiking neurons. The computational power of cortical neural P systems is investigated. It is proved that cortical neural P systems are equivalent to Turing machines as number generators. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Turing machines

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A Fast-trained and Generalized Spiking Neural Network for Robotic Gas Source Localization

A Fast-trained and Generalized Spiking Neural Network for Ro...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Chiyang Liu Xinxing Chen Jian Huang Hubei Key Laboratory of Brain-inspired Intelligent Systems and the Key Laboratory for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Mobile robots for Gas Source Localization (GSL) tasks are a safer alternative than human and animal rescuers in hazardous scenarios. Existing research primarily concentrates on rule-based algorithms or conventional artificial neural networks (ANNs). However, these approaches are either inapplicable in cluttered environments or undeployable due to their high energy consumption and the demand for substantial computational resources. This paper introduces the application of energy-efficient spiking neural networks (SNNs) to address robotic GSL tasks. A pipeline is proposed to train SNNs with deep Q learning and a pretrain-finetune paradigm. To facilitate the training process, a small dataset of gas dispersion is generated utilizing openFoam and GADEN, a high-fidelity simulator for gas dispersion. Data from a simplified plume model are leveraged to pretrain an ANN, the activation function of which gradually transitions from a bounded rectified linear unit (bReLU) to a step function. Subsequently, an SNN initialized with the ANN parameters undergoes finetuning on the GADEN-based dataset. The training pipeline significantly reduces training time compared to direct training of SNNs. The trained SNN is validated within the GADEN simulation environment and compared to three different models, demonstrating promising performance and superior generalization despite limited training data.

关键词： Training Location awareness Q-learning Mechatronics Pipelines Training data Spiking neural networks Data models Mobile robots Dispersion

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