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Chinese control Conference (CCC)

作者： Mengting Ao Changhe Li Shengxiang Yang School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Computer Science and Informatics De Montfort University Leicester United Kingdom

Aiming at the prediction of truck travel time in open pit mines, we established a prediction model based on long short-term memory(LSTM). This model fully accounts for 11 factors, including the nature of trucks, weather, road conditions, and driver's behaviors, as well as the influence of neighbor road segments in the route on the current predicted road segment. The experiment shows that the error of the LSTM prediction model is significantly reduced compared with SVR and BP models. In addition, the maximum absolute mean error under different conditions is less than 12 seconds.

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Carbonized bacterial cellulose/FeMn composite as efficient catalyst toward contaminant degradation: The crucial role of hydrogen reduction

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Chemosphere 2023年第1期335卷 139176页

作者： Liu, Fei You, Jikang Duan, Chongsen Li, Zhe Xu, Huacheng State Key Laboratory of Lake Science and Environment Nanjing Institute of Geography and Limnology Chinese Academy of Sciences Nanjing210008 China Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse School of Environmental and Biological Engineering Nanjing University of Science and Technology 200 Xiao Ling Wei Nanjing210094 China CAS Key Lab of Reservoir Environment Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China

The structure especially the active site manipulation of Fenton-like catalysts was essential for the efficient removal of organic contaminants in the aquatic environment. In this study, the carbonized bacterial cellulose/FeMn oxide composite (CBC@FeMnOx) were synthetized and modified by hydrogen (H2) reduction to obtain the carbonized bacterial cellulose/FeMn composite (CBC@FeMn), with emphasis on the processes and mechanisms for atrazine (ATZ) attenuation. The results showed that H2 reduction did not change the microscopic morphology of the composites but destroy the Fe–O and Mn–O structures. Compared with the CBC@FeMnOx composite, the H2 reduction could promote the removal efficiency from 62% to 100% for CBC@FeMn, as well as the enhancement of degradation rate from 0.021 min−1 to 0.085 min−1. The quenching experiments and electron paramagnetic resonance (EPR) displayed that the hydroxyl radicals (•OH) was the major contributor for ATZ degradation. The investigation for Fe and Mn species indicated that H2 reduction could increase the content of Fe(II) and Mn(III) in the catalyst, thus improving the generation of •OH and accelerating the cycle process between Fe(III)/Fe(II). Owing to the excellent reusability and stability, it was indicated that the H2 reduction can be considered as an efficient way to regulate the chemical valence of the catalyst, thus enhancing the removal efficiency of aquatic contaminants. © 2023 Elsevier Ltd

关键词： Oxidation

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A Test Suite and An Optimizer for Dietary Nutrition Optimization Problem: From Constrained Many-Objective Perspective

A Test Suite and An Optimizer for Dietary Nutrition Optimiza...

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Congress on Evolutionary Computation

作者： Yaqi Ti Changhe Li Shengxiang Yang School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Computer Science and Informatics De Montfort University Leicester United Kingdom

With increasing people who suffer from diet-related diseases, providing suggestions for personal daily nutrient-dense intake is highly expected. However, current dietary nutrition models are less precise, and dietary nutrition optimizers usually fail to give satisfactory solutions. Therefore, we construct a constrained many-objective nutrition model with more precise nutrient assessments and a scalable constrained many-objective benchmark set. This test suite has great flexibility in evaluating algorithms' performance on high dimensional search and objective spaces with some feasible region fragments. We also propose a kd-tree based dynamic constrained many-objective evolutionary algorithm to search for customized food combinations according to personal daily consumption and intake preference. Experiments show that our algorithm has better diversity maintenance ability in high dimension space.

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Monotonic quantum-to-classical transition enabled by positively correlated biphotons

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Physical Review A 2017年第6期95卷 062341-062341页

作者： Rui-Bo Jin Guo-Qun Chen Hui Jing Changliang Ren Pei Zhao Ryosuke Shimizu Pei-Xiang Lu Laboratory of Optical Information Technology and School of Material Science and Engineering Wuhan Institute of Technology Wuhan 430205 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics and Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha 410081 China Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing 400714 China University of Electro-Communications 1-5-1 Chofugaoka Chofu Tokyo 182-8585 Japan

Multiparticle interference is a fundamental phenomenon in the study of quantum mechanics. It was discovered in a recent experiment [Y.-S. Ra et al., Proc. Natl. Acad. Sci. USA 110, 1227 (2013)] that spectrally uncorrelated biphotons exhibited a nonmonotonic quantum-to-classical transition in a four-photon Hong-Ou-Mandel (HOM) interference. In this work, we consider the same scheme with spectrally correlated photons. By theoretical calculation and numerical simulation, we found the transition not only can be nonmonotonic with negatively correlated or uncorrelated biphotons, but also can be monotonic with positively correlated biphotons. The fundamental reason for this difference is that the HOM-type multiphoton interference is a differential-frequency interference. Our study may shed new light on understanding the role of frequency entanglement in multiphoton behavior.

关键词： Entanglement detection Entanglement manipulation Quantum entanglement

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Zero-Sum Game Based Secure Tracking control of UAV Against FDI Attacks Using Fixed-Time Convergent Reinforcement Learning

Zero-Sum Game Based Secure Tracking Control of UAV Against F...

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IEEE Conference on Decision and control

作者： Zhenyu Gong Feisheng Yang Dongrui Wu Research & Development Institute of Northwestern Polytechnical University in Shenzhen School of Automation Northwestern Polytechnical University Xi'an China Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial and Automation Huazhong University of Science and Technology Wuhan China

In this paper, a fixed-time convergent reinforcement learning (RL) algorithm is developed to realize the secure tracking control of the unmanned aerial vehicle (UAV) via the zero-sum game for the first time. To mitigate FDI attack on actuators that may cause the UAV to deviate from the reference trajectory, a zero-sum differential game framework is built in which the secure controller tries to minimize the common performance function, yet the attacker plays a contrary role. Obtaining the optimal secure tracking controller depends on solving the Hamilton-Jacobi-Isaacs (HJI) equation related to the zero-sum game. Therefore, a critic-only online RL algorithm is proposed that can converge in a fixed time interval, with the corresponding convergence proof provided. A simulation example is given to show the effectiveness of the raised method.

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Learning of long-horizon sparse-reward robotic manipulator tasks with base controllers

arXiv

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arXiv 2020年

作者： Wang, Guangming Xin, Minjian Wu, Wenhua Liu, Zhe Wang, Hesheng Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China Department of Computer Science and Engineering University of California San Diego La Jolla CA92093 United States Department of Computer Science and Technology University of Cambridge CambridgeCB2 1TN United Kingdom

Deep Reinforcement Learning (DRL) enables robots to perform some intelligent tasks end-to-end. However, there are still many challenges for long-horizon sparse-reward robotic manipulator tasks. On the one hand, a sparse-reward setting causes exploration inefficient. On the other hand, exploration using physical robots is of high cost and unsafe. In this paper, we propose a method of learning long-horizon sparse-reward tasks utilizing one or more existing traditional controllers named base controllers in this paper. Built upon Deep Deterministic Policy Gradients (DDPG), our algorithm incorporates the existing base controllers into stages of exploration, value learning, and policy update. Furthermore, we present a straightforward way of synthesizing different base controllers to integrate their strengths. Through experiments ranging from stacking blocks to cups, it is demonstrated that the learned state-based or image-based policies steadily outperform base controllers. Compared to previous works of learning from demonstrations, our method improves sample efficiency by orders of magnitude and improves the performance. Overall, our method bears the potential of leveraging existing industrial robot manipulation systems to build more flexible and intelligent controllers. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Forest Height Mapping Combining GEDI, ALOS-2, Sentinel-1/2, and Ancillary Data

Forest Height Mapping Combining GEDI, ALOS-2, Sentinel-1/2, ...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： João E. Pereira-Pires Juan Guerra-Hernández João M. N. Silva José M. Fonseca Raffaella Guida André Mora Centre of Technology and Systems/UNINOVA School of Science and Technology and Associated Lab of Intelligent Systems (LASI) NOVA University of Lisbon Caparica Portugal Forest Research Centre and Associate Laboratory TERRA School of Agriculture University of Lisbon Lisbon Portugal Surrey Space Centre University of Surrey Guildford UK

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

The impacts of the climate change in the society make forest monitoring increasingly important. Consequently, there is a growing interest in mapping variables as the Forest Height (FH). The direct measurement of the FH through field campaigns is expensive and difficult to scale. Alternatively, Airborne Laser Scanning (ALS) campaigns can be used to map it, however they share the same disadvantages of the previous approach. Therefore, Remote Sensing (RS) data have been used for local and large-scale mapping of the FH. In this paper a Regression Methodology (RM) that combines GEDI, ALOS-2, Sentinel-1/2, and ancillary data is proposed for mapping the FH in Mediterranean forests. The proposed RM, tested for the 15 regions of interest, achieves a RMSE/rRMSE of 4.95m/33.93%, when evaluated with GEDI data, and 5.11m/41.70%, when evaluated with ALS data.

关键词： Radar remote sensing Climate change Measurement by laser beam Forestry Radar Apertures Remote sensing

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Attractor flow analysis for recurrent neural network with back-to-back memristors

Attractor flow analysis for recurrent neural network with ba...

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IEEE Symposium on Foundations of Computational Intelligence, FOCI

作者： Gang Bao Zhigang Zeng School of Automation Huazhong University of Science and Technology Wuhan Hubei China Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education Wuhan China college of Electrical Engineering and New Energy China Three Gorges University Yichang China

ISBN: (纸本)9781479944903

Memristor is a nonlinear resistor with the character of memory and is proved to be suitable for simulating synapse of neuron. This paper introduces two memristors in series with the same polarity (back-to-back) as simulator for neuron's synapse and presents the model of recurrent neural networks with such back-to-back memristors. By analysis techniques and fixed point theory, some sufficient conditions are obtained for recurrent neural network having single attractor flow and multiple attractors flow. At last, simulation with numeric examples is presented to illustrate our results.

关键词： Memristors Stability analysis Recurrent neural networks Resistance Numerical models

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Joint Computation Offloading and Resource Allocation for LEO Satellite Networks Using Hierarchical Multi-Agent Reinforcement Learning

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IEEE Transactions on Cognitive Communications and Networking 2024年

作者： Lai, Junyu Liu, Huashuo Xu, Guoyao Jiang, Weiwei Wang, Xiong Jiang, Dingde University of Electronic Science and Technology of China School of Aeronautics and Astronautics China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province Chengdu611731 China University of Electronic Science and Technology of China School of Aeronautics and Astronautics Chengdu611731 China Ministry of Education Key Laboratory of Universal Wireless Communications China Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing100876 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China

The integration of edge computing with LEO satellite broadband networks (LSBNs) offers a transformative potential, yet remains underexplored in the optimization of joint computation offloading and resource allocation (JCORA). This problem is compounded by the issues of load imbalance and hybrid action spaces. To tackle them, we firstly propose a multi-level edge computing architecture that leverages the inter-satellite links to enable collaborative offloading among neighboring satellites, thereby enhancing global resource utilization and load balance in LSBNs. Moreover, existing studies demonstrate the benefits of deep reinforcement learning (DRL) for JCORA optimization but struggle with the complexities of hybrid action spaces. To address this, we elaborate a novel hierarchical multi-agent DRL (HMADRL) framework that decomposes the JCORA problem into two-layered subproblems, namely global computation offloading and local resource allocation. This decomposition effectively mitigates the challenge posed by hybrid action spaces. The computation offloading subproblem is formulated as a delayed-reward partially observable Markov decision process, optimized by using multi-agent deep Q-networks specialized in discrete action outputs. Meanwhile, the resource allocation subproblem is addressed through the deep deterministic policy gradient model, adept at handling continuous actions. Extensive experiments validate our approach, demonstrating improvements in delay reduction, outrage rate, and load balancing compared to baselines. © 2015 IEEE.

关键词： Markov processes

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Dynamic Reconfiguration of Distribution Network Based on Temporal Constrained Hierarchical Clustering and Fireworks Algorithm

Dynamic Reconfiguration of Distribution Network Based on Tem...

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IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia)

作者： Yumin Zhang Xingquan Ji Jiabin Xu Ziyang Yin Xueshan Han Chao Zhang College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China Dongying Power Supply Company State Grid Dongying China Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China

ISBN: (数字)9781728143033

ISBN: (纸本)9781728143040

For the dynamic reconfiguration problem of distribution networks, the handling of switching cost constraint and the division of time period are the two key aspects that affect the performance of the reconfiguration algorithm. In this paper, an improved temporal constrained hierarchical clustering algorithm for dynamic reconfiguration of distribution networks is proposed. Firstly, the improved hierarchical clustering approach considering the temporal constraint is used to divide the operating status of the distribution network into multiple time intervals according to the similarity of loads and the output power of distributed generations (DGs). The dynamic reconfiguration of distribution networks is then transformed into multiple single-stage static reconfiguration problems, and the cluster center is used to approximately represent the load state and DG output state of the period. In order to reduce the number of infeasible solutions and poor solutions, a coding method based on heuristic rules to reduce the solution space of distribution network reconfiguration is proposed. An improved fireworks algorithm based on heuristic rules is proposed to solve the reconfiguration problem. Finally, a modified IEEE-33 test system is used to verify the validity of the proposed method.

关键词： Explosions Heuristic algorithms Switches Fireworks algorithm Power system dynamics Sparks Clustering algorithms

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