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arXiv 2024年

作者： Gatto, Elaine Cecília Nakano, Felipe Kenji Read, Jesse Ferrandin, Mauri Cerri, Ricardo Vens, Celine Department of Computer Science Federal University of São Carlos SP São Carlos13565-905 Brazil Faculty of Medicine Katholieke Universiteit Leuven West Flanders Kortrijk8500 Belgium École Polytechnique Polytechnic Institute of Paris Palaiseau Paris91120 France Department of Control Automation and Computing Engineering Federal University of Santa Catarina SC Blumenau89036-002 Brazil Institute of Mathematical and Computing Sciences University of São Paulo SP São Carlos13566-590 Brazil

Multi-label classification is a type of supervised machine learning that can simultaneously assign multiple labels to an instance. To solve this task, some methods divide the original problem into several sub-problems (local approach), others learn all labels at once (global approach), and others combine several classifiers (ensemble approach). Regardless of the approach used, exploring and learning label correlations is important to improve the classifier predictions. Ensemble of Classifier Chains (ECC) is a well-known multi-label method that considers label correlations and can achieve good overall performance on several multi-label datasets and evaluation measures. However, one of the challenges when working with ECC is the high dimensionality of the label space, which can impose limitations for fully-cascaded chains as the complexity increases regarding feature space expansion. To improve classifier chains, we propose a method to chain disjoint correlated label clusters obtained by applying a partition method in the label space. During the training phase, the ground truth labels of each cluster are used as new features for all of the following clusters. During the test phase, the predicted labels of clusters are used as new features for all the following clusters. Our proposal, called Label Cluster Chains for Multi-Label Classification (LCC-ML), uses multi-label Random Forests as base classifiers in each cluster, combining their predictions to obtain a final multi-label classification. Our proposal obtained better results compared to the original ECC. This shows that learning and chaining disjoint correlated label clusters can better explore and learn label correlations. © 2024, CC BY.

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Quantum error precompensation for quantum noisy channels

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Physical Review A 2022年第4期106卷 042440-042440页

作者： Chengjie Zhang Liangsheng Li Guodong Lu Haidong Yuan Runyao Duan School of Physical Science and Technology Ningbo University Ningbo 315211 China Science and Technology on Electromagnetic Scattering Laboratory Beijing 100854 China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong Institute for Quantum Computing Baidu Research Beijing 100193 China

Most previous efforts on quantum error correction focused on either extending classical error-correction schemes to the quantum regime by performing a perfect correction on a subset of errors or seeking a recovery operation to maximize the fidelity between an input state and its corresponding output state of a noisy channel. There are few results concerning quantum error precompensation. Here we design an error-precompensated input state for an arbitrary quantum noisy channel and a given target output state. By following a procedure, the required input state, if it exists, can be analytically obtained in single-partite systems. Furthermore, we also present semidefinite programs to numerically obtain the error-precompensated input states with maximal fidelities between the target state and the output state. The numerical results coincide with the analytical results.

关键词： Quantum channels Quantum control Quantum error correction

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Coalitional MPC with predicted topology transitions ⁎

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IFAC-PapersOnLine 2020年第2期53卷 3342-3347页

作者： E. Masero J.M. Maestre M. Francisco E.F. Camacho Systems Engineering and Automation Department University of Seville Seville Spain Computing and Automation Department University of Salamanca Salamanca Spain

This paper proposes a novel hierarchical coalitional MPC technique, where transitions to the best communication topology are considered over the prediction horizon. For this reason, a new variable, called transition horizon, is added to the optimization problem to compute the optimal instant to introduce a new topology. Consequently, local controllers can anticipate topology transitions and adapt their trajectories whilst optimizing their local interests. Furthermore, stability guarantees in the closed-loop control of each coalition are provided. The benefits of this control method are shown via a simulated non-linear eight-coupled tanks plant.

关键词： Model predictive control Multi-agent systems Networked control LMI Stability

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Generative AI for Deep Reinforcement Learning: Framework, Analysis, and Use Cases

arXiv

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arXiv 2024年

作者： Sun, Geng Xie, Wenwen Niyato, Dusit Mei, Fang Kang, Jiawen Du, Hongyang Mao, Shiwen College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Automation Guangdong University of Technology Guangzhou510006 China Department of Electrical and Electronic Engineering The University of Hong Kong 999077 Hong Kong Department of Electrical and Computer Engineering Auburn University Auburn36830 United States

As a form of artificial intelligence (AI) technology based on interactive learning, deep reinforcement learning (DRL) has been widely applied across various fields and has achieved remarkable accomplishments. However, DRL faces certain limitations, including low sample efficiency and poor generalization. Therefore, we present how to leverage generative AI (GAI) to address these issues above and enhance the performance of DRL algorithms in this paper. We first introduce several classic GAI and DRL algorithms and demonstrate the applications of GAI-enhanced DRL algorithms. Then, we discuss how to use GAI to improve DRL algorithms from the data and policy perspectives. Subsequently, we introduce a framework that demonstrates an actual and novel integration of GAI with DRL, i.e., GAI-enhanced DRL. Additionally, we provide a case study of the framework on UAV-assisted integrated near-field/far-field communication to validate the performance of the proposed framework. Moreover, we present several future directions. Finally, the related code is available at: https://***/GAI-enhanced-DRL. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Computationally Expensive High-Dimensional Multiobjective Optimization via Surrogate-Assisted Reformulation and Decomposition

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Pan, Linqiang Lin, Jianqing Wang, Handing He, Cheng Tan, Kay Chen Jin, Yaochu School of Artificial Intelligence and Automation Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China Huazhong University of Science and Technology Wuhan China School of Artificial Intelligence Xidian University Xi’an China School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan China Department of Computing The Hong Kong Polytechnic University Hong Kong Hong Kong School of Engineering Trustworth and General Artificial Intelligence Laboratory Westlake University Hangzhou China

In recent decades, various surrogate-assisted evolutionary algorithms (SAEAs) have been proposed to solve computationally expensive multiobjective optimization problems (EMOPs). Nevertheless, designing an SAEA to handle high-dimensional EMOPs and balance convergence, diversity, and computational complexity remains challenging. Here, we propose a two-phase SAEA (TP-SAEA), which follows the idea of convergence first and diversity second, for solving high-dimensional EMOPs. In Phase I, a surrogate-assisted problem reformulation method is proposed to fast-track the Pareto optimal set in association with some reference solutions. Specifically, the high-dimensional EMOP is reformulated into an expensive single-objective one with low-dimensional decision space. Then, the surrogate-assisted optimization is utilized to obtain well-converged solutions. In Phase II, the high-dimensional EMOP is decomposed into two subproblems to explore subregions of the decision space that can effectively promote the diversity of the solutions. The two subproblems are optimized independently via surrogate-assisted optimization, aiming to push the population towards different regions of the Pareto optimal front. Experiments are conducted on EMOPs with 100 to 500 decision variables compared with four state-of-the-art SAEAs. The proposed TP-SAEA obtains well-converged and diverse solutions with only 509 real function evaluations. Moreover, its superiority is examined in six real-world instances with up to 12,000 decision variables. IEEE

关键词： Iron

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Quantum error pre-compensation for quantum noisy channels

arXiv

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arXiv 2023年

作者： Zhang, Chengjie Li, Liangsheng Lu, Guodong Yuan, Haidong Duan, Runyao School of Physical Science and Technology Ningbo University Ningbo315211 China Science and Technology on Electromagnetic Scattering Laboratory Beijing100854 China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong Institute for Quantum Computing Baidu Research Beijing100193 China

Most previous efforts of quantum error correction focused on either extending classical error correction schemes to the quantum regime by performing a perfect correction on a subset of errors, or seeking a recovery operation to maximize the fidelity between a input state and its corresponding output state of a noisy channel. There are few results concerning quantum error pre-compensation. Here we design an error pre-compensated input state for an arbitrary quantum noisy channel and a given target output state. By following a procedure, the required input state, if it exists, can be analytically obtained in single-partite systems. Furthermore, we also present semidefinite programs to numerically obtain the error pre-compensated input states with maximal fidelities between the target state and the output state. The numerical results coincide with the analytical results. Copyright © 2023, The Authors. All rights reserved.

关键词： Error correction

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Distributed Load Shedding via Differentially Private Average Consensus Algorithm

Distributed Load Shedding via Differentially Private Average...

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Chinese Control and Decision Conference, CCDC

作者： Zixin Qiao Fanghong Guo Xiping Pan Yue Sun Lei Wang Shanghai Institute of Computing Technology Shanghai China Department of Automation Zhejiang University of Technology Hangzhou China Shandong Institute of Blockchain Jinan China College of Control Science and Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9781665478977

This paper presents a distributed load shedding strategy for islanded active distribution power system, where the average consensus algorithm acts as a key tool to discover the required global information for load shedding. For privacy concerns, we propose a new differentially private average consensus algorithm, where a distributed shuffling mechanism based on the Pailler cryptosystem is established to initialize the initial states for the subsequent stage of running standard average consensus. For the proposed algorithm, both rigorous provable differential privacy and accuracy guarantee are established, with an improved privacy/accuracy trade-off than the existing relevant frameworks. Particularly, it is shown that given any expected differential privacy levels, the achievable mean-square computation error is inversely proportional to the network size, in contrast with the optimal error achieved in the literature that is independent of the network size. A numerical example and an application to the distributed load shedding for an IEEE 33-bus power system are used to validate the effectiveness of the proposed approach.

关键词： Privacy Differential privacy Load shedding Consensus algorithm Power systems Standards

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Sparse Training Data-Based Hyperspectral Image Super Resolution Via ANFIS Interpolation

Sparse Training Data-Based Hyperspectral Image Super Resolut...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Jing Yang Changjing Shang Lu Chen Pan Su Qiang Shen School of Automation and Software Engineering Shanxi University Taiyuan China Department of Computer Science Aberystwyth University Aberystwyth UK Insti. of Big Data Science & Industry Shanxi University Taiyuan China Department of Computer Hebei Key Laboratory of Knowledge Computing for Energy & Power Baoding North China Electric Power University China

Hyperspectral image super resolution aims to improve the spatial resolution of given hyperspectral images, which has become a highly attractive topic in the field of image processing. Existing techniques typically focus on super resolution with sufficient training data. However, restricted by data acquisition conditions, certain hyperspectral images or band images are very different to obtain, resulted in insufficient training data. In order to solve this problem, a new hyperspectral image super resolution method is proposed in this paper in an effort to conduct the super resolution task over insufficient (sparse) training data, by applying the recently introduced ANFIS (Adaptive Network-based Fuzzy Inference System) interpolation method. Particularly, the training dataset is divided into several subsets. For the subsets with sufficient training data, the relevant ANFIS models are trained using standard ANFIS learning algorithm, while for the subsets with sparse training data, the corresponding ANFIS models are interpolated through the use of ANFIS interpolation. Experimental results indicate that compared with the methods using sufficient training data, the proposed method can achieve very similar result, showing its effectiveness for situations where only sparse training data is available.

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Minimum displacements for cloth-obstacle penetration resolving 37

Minimum displacements for cloth-obstacle penetration resolvi...

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37th Annual Conference on European Association for Computer Graphics, EUROGRAPHICS 2016

作者： Sun, Liming Nyberg, Timo R. Xiong, Gang Ye, Juntao NLPR-LIAMA Institute of Automation CAS China Cloud Computing Center CAS Dongguan China SKL-MCCS Institute of Automation CAS China School of Automation Foshan University China Department of Industrial Engineering and Management Aalto University Finland

Pre-existing penetrations often show up in many applications, particularly in garments fitting. The popular continuous collision detection (CCD) based methods are incapable of handling them, as there is no history information to rely on. On the other hand, surfaces of human bodies have normals defined to designate their orientation (i.e. front- and back-face), which are totally overlooked by CCD methods (thus they are orientation-free). In this paper we present a history-free method for separating two penetrating meshes, given that one of them represents a rigid object and has clarified surface orientation. This method computes all edge-face (E-F) intersections with discrete collision detection, and identifies illegal vertices with the help of surface orientation, and then builds a number of penetration stencils. On response, the stencil vertices are relocated into a penetration-free state, via a global displacement minimizer. The proposed algorithm outperforms existing methods for handling solid/cloth collisions, thus is an effective tool for applications like virtual-try-on and example-based garment animation synthesis. © 2016 The Eurographics Association.

关键词： Animation

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Segmentation Model Approaches using Cardiac Magnetic Resonance Images: A Review

TechRxiv

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TechRxiv 2022年

作者： Khan, Amjad Nfai, Dayang Wang, Yin Chai Lim, P.C. Ullah, Faheem Javid Ahmad, Tariq Deapartment of Computing Abasyn University Islamabad Campus Pakistan UNIMAS Sarawak Malaysia School of Automation and Control Engineering SCUT China Department of Computer Science University of Science and Technology Bannu Pakistan School of Information and Communications Engineering Guilin University China

Automatic segmentation solution is the process of detecting and extracting information to simplify the representation of Cardiac Magnetic Resonance images (CMRI) of Left Ventricle (LV) contour. This segmented information, using CMR images, helps to reduce the segmentation error between expert and automatic segmented contours. The error represents missing region values calculated in percentages after segmenting a cardiac LV contour. This review paper will discuss the major three segmentation approaches, namely manual approach, semiautomatic, and fully automatic, along with the segmentation models, namely image-based models, region-based models, edge-based models, deformable-based models, active shape-based models (ASM), active contour-based models (ACM), level set-based models (LSM), and variational LSM (VLSM). The review deeply explains the performance of segmentation models using different techniques. Furthermore, the review compares 122 studies on segmentation model approaches, i.e., 16 from 2004 to 2010, 40 from 2011 to 2016, and 63 from 2017 to 2021, and 3 other related studies were conducted for LV contour segmentation, cardiac function, area-at-risk (AAR) identification, scar tissue classification, oedema tissue classification, and identification via presence, size, and location. Given the large number of articles on CMR-LV images that have been published, this review conducted a critical analysis and found a gap for researchers in the areas of LV localization, LV contour segmentation, cardiac function, and oedoema tissue classification and segmentation. Regarding critical analysis, this paper summarised a research gap and made useful suggestions for new CMR-LV researchers. although a timely reviewed study can lead to cardiac segmentation challenges, which will be discussed in each review section. © 2022, CC BY.

关键词： Magnetic resonance imaging

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