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4th International Conference on Information science, Parallel and Distributed Systems, ISPDS 2023

作者： Jiang, Chengjia Wang, Tao Li, Sien Wang, Jinyang Wang, Shirui Antoniou, Antonios Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China Fujian Education Institutions Wuyi University The Key Laboratory of Cognitive Computing and Intelligent Information Processing Wuyishan China College of Computer and Data Science Fuzhou University Fuzhou China European University Cyprus Department of Computer Science and Engineering Nicosia Cyprus

ISBN: (纸本)9798350337181

We address the problem of learning new classes for semantic segmentation models from few examples, which is challenging because of the following two reasons. Firstly, it is difficult to learn from limited novel data to capture the underlying class distribution. Secondly, it is challenging to retain knowledge for existing classes and to avoid catastrophic forgetting. For learning from limited data, we propose a pseudo-labeling strategy to augment the few-shot training annotations in order to learn novel classes more effectively. Given only one or a few images labeled with the novel classes and a much larger set of unlabeled images, we transfer the knowledge from labeled images to unlabeled images with a coarse-to-fine pseudo-labeling approach in two steps. Specifically, we first match each labeled image to its nearest neighbors in the unlabeled image set at the scene level, in order to obtain images with a similar scene layout. This is followed by obtaining pseudo-labels within this neighborhood by applying classifiers learned on the few-shot annotations. In addition, we use knowledge distillation on both labeled and unlabeled data to retain knowledge on existing classes. We integrate the above steps into a single convolutional neural network with a unified learning objective. Extensive experiments on the Cityscapes and KITTI datasets validate the efficacy of the proposed approach in the self-driving domain. Code is available from https://***/ChasonJiang/FSCILSS. © 2023 IEEE.

关键词： Distillation

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Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced control Applications

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 18-36页

作者： Ding Wang Ning Gao Derong Liu Jinna Li Frank L.Lewis IEEE the Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijing Laboratory of Smart Environmental Protectionand Beijing Institute of Artificial IntelligenceBeijing University of TechnologyBeijing 100124China the School of System Design and Intelligent Manufacturing Southern University of Science and TechnologyShenzhen 518055China the Department of Electrical and Computer Engineering University of Illinois at ChicagoChicago IL 60607 USA the School of Information and Control Engineering Liaoning Petrochemical UniversityFushun 113001China the UTA Research Institute the University of Texas at ArlingtonArlington TX 76118 USA

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, ***, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation ***, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

关键词： Adaptive dynamic programming(ADP) advanced control complex environment data-driven control event-triggered design intelligent control neural networks nonlinear systems optimal control reinforcement learning(RL)

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A Simultaneous Wireless Information and Power Transfer-based Multi-hop Clustering Routing Protocol for RF Energy Harvesting-Cognitive Radio Sensor Networks

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第3期8卷 991-1007页

作者： Wang, Ji-Hong Su, Xi-Tong Ge, Yi-Yang Chen, Yeh-Cheng Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University China School of Electrical Engineering Northeast Electric Power University 169 Changchun Road Jilin132012 China Department of Computer Science University of California DavisCA001313 United States

Radio frequency energy harvesting (RF EH) and clustering are promising solutions to solve the energy constraint problem faced by cognitive radio sensor networks (CRSNs). However, study on clustering routing protocol design for RF EH-CRSNs is still in the initial stage, and there lacks of solutions for inter-cluster routing problem, which restricts the network scalability. In addition, unbalanced harvested energy and energy consumption may result in premature death of nodes in key position, which will significantly reduce the network performance. In this paper, a simultaneous wireless information and power transfer (SWIPT)-based multi-hop clustering routing protocol (SMCRP) is proposed for RF EH-CRSNs to well balance the residual energy distribution among nodes and postpone node death. Apart from the RF energy harvested from dedicated or ambi-ent energy sources, the energy obtained through intra-cluster and inter-cluster SWIPT is also considered to facilitate high-quality cluster heads selection, cluster formation and inter-cluster route establishment. Power splitting ratio is derived based on the residual energy of CRSNs nodes and their energy consumption to enable effective implementation of SWIPT in CRSNs. Simulation results show that SMCRP is effective in balancing the residual energy distribution among nodes and improving node sustainability. To be specific, compared with current clustering protocols, the network lifetime is extended by 64.07% while intra-cluster energy balance degree and network energy balance degree are improved by 15.1% and 7.82%, respectively. © 2023 ISSN 2414-8105 (Online).

关键词： Cognitive radio

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An Experimentally Validated Model of the Propeller Force Accounting for Cross Influences on Multi-Rotor Aerial Systems

An Experimentally Validated Model of the Propeller Force Acc...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Barbara Bazzana Ralph Brantjes Chiara Gabellieri Antonio Franchi Robotics and Mechatronics Department Electrical Engineering Mathematics and Computer Science (EEMCS) Faculty University of Twente Enschede The Netherlands Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

In this paper, we propose a model for the thrust coefficient of propellers that can take into account cross-influence between adjacent propellers. The aerodynamic interaction between propellers in multirotor aerial vehicles reduces the thrust they can produce. The influence between propellers depends on their relative positioning and orientation, which are taken into account by the proposed model. It is validated on measurements collected by a force sensor mounted on a propeller for different configurations of the adjacent propellers in a support structure. In this work, we focus on configurations with small relative orientations. Results show that the proposed model outperforms the traditional constant model in terms of thrust prediction on the data we collected, and it performs better than other models with fewer parameters, being the only one with less than 10% maximum percentage error.

关键词： Training Adaptation models Torque Propellers Atmospheric modeling Force Velocity control

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Adaptive-Grained Label Distribution Learning 39

Adaptive-Grained Label Distribution Learning

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Lu, Yunan Li, Weiwei Liu, Dun Li, Huaxiong Jia, Xiuyi School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China School of Economics and Management Southwest Jiaotong University Chengdu China Department of Control Science and Intelligence Engineering Nanjing University Nanjing China

ISBN: (纸本)157735897X

Label polysemy, where an instance can be associated with multiple labels, is common in real-world tasks. LDL (label distribution learning) is an effective learning paradigm for handling label polysemy, where each instance is associated with a label distribution. Although numerous LDL algorithms have been proposed and achieved satisfactory performance on most existing datasets, they are typically trained directly on the collected label distributions which often lack quality guarantees in real-world tasks due to annotator subjectivity and algorithm assumptions. Consequently, direct learning from such uncertain label distributions can lead to unpredictable generalization performance. To address this problem, we propose an adaptive-grained label distribution learning framework whose main idea is to extract relatively reliable supervision information from unreliable label distributions, and thus the label distribution learning task can be decomposed into three subtasks: coarsening label distributions, learning coarse-grained labels and refining coarse-grained labels. In this framework, we design an adaptive label coarsening algorithm to extract an optimal coarsen-grained labels and a label refining function to enhance the coarse-grained label into the final label distributions. Finally, we conduct extensive experiments on real-world datasets to demonstrate the advantages of our proposal. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Supervised learning

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A Method for Constructing an Olfactory Dataset Based on fNIRS

A Method for Constructing an Olfactory Dataset Based on fNIR...

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International Conference on Systems and Informatics (ICSAI)

作者： Wei Li Haobo He Guang Li Lidan Kuang Jin Zhang School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China School of Control Science and Engineering Zhejiang University Hangzhou China

Biomimetic olfaction models have been applied to various fields such as image recognition, text classification, electroencephalography (EEG) recognition, and spatio-temporal sequence prediction. However, the challenge of obtaining olfactory data complicates the construction of olfactory datasets, ultimately resulting in a scarcity of applications for biomimetic olfaction models in olfactory tasks. In this work, we begin by conducting a comprehensive comparison of existing biomimetic olfactory models from various aspects such as structure, degree of biomimicry, and performance. Following this, prefrontal lobe olfactory data from ten subjects were collected for three different odors using functional Near-Infrared Spectroscopy (fNIRS). Finally, the data underwent denoising through the use of various data processing methods, and the quantity of the olfactory dataset was expanded. The olfactory dataset constructed in this study can be used to evaluate the performance of biomimetic olfactory models in processing olfactory tasks, which may aid in advancing the development of these models in real-world olfactory applications.

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Futures Price Forecasting Based on LSTM Model Using Public Opinion Sentiment Analysis

Futures Price Forecasting Based on LSTM Model Using Public O...

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第42届中国控制会议

作者： Panfeng Shi Shifan Song Qi Lang Xiaodong Liu School of Control Science and Engineering Dalian University of Technology School of Computer Science and Information Technology Northeast Normal University

As the economy continues to develop and the futures market grows,more and more attention is being paid to the subject of futures price *** factors influencing the futures market are complex,and the existing forecasting models tend to ignore the acquisition of information other than market ***,this paper proposes a futures price forecasting model based on sentiment analysis of online public *** futures products are selected for training and validation of the model,and the Chinese posts in the futures bar are used as input for public opinion,which is processed by the investor sentiment analysis fusion module to obtain sentiment values as a priori information for the *** groups of control experiments are set up to verify the validity of the proposed model and to compare the effect of sentiment feature ***,the experimental results show that the proposed model incorporating public opinion gets better results in the selected futures product price prediction task.

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A Learning-based Incentive Mechanism for Mobile AIGC Service in Decentralized Internet of Vehicles

A Learning-based Incentive Mechanism for Mobile AIGC Service...

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IEEE Conference on Vehicular Technology (VTC)

作者： Jiani Fan Minrui Xu Ziyao Liu Huanyi Ye Chaojie Gu Dusit Niyato Kwok-Yan Lam School of Computer Science and Engineering Nanyang Technological University Singapore College of Control Science and Engineering Zhejiang University People’s Republic of China

Artificial Intelligence-Generated Content (AIGC) refers to the paradigm of automated content generation utilizing AI models. Mobile AIGC services in the Internet of Vehicles (IoV) network have numerous advantages over traditional cloud-based AIGC services, including enhanced network efficiency, better reconfigurability, and stronger data security and privacy. Nonetheless, AIGC service provisioning frequently demands significant resources. Consequently, resource-constrained roadside units (RSUs) face challenges in maintaining a heterogeneous pool of AIGC services and addressing all user service requests without degrading overall performance. Therefore, in this paper, we propose a decentralized incentive mechanism for mobile AIGC service allocation, employing multi-agent deep reinforcement learning to find the balance between the supply of AIGC services on RSUs and user demand for services within the IoV context, optimizing user experience and minimizing transmission latency. Experimental results demonstrate that our approach achieves superior performance compared to other baseline models.

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Prediction of Boiler Heat-Conducting Oil Temperature Based on Multi-Modality Fuzzy Cognitive Maps

Prediction of Boiler Heat-Conducting Oil Temperature Based o...

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第35届中国控制与决策会议

作者： Jiaqing Li Qi Lang Yan Fang Xiaodong Liu School of Control Science and Engineering Dalian University of Technology School of Computer Science and Information Technology Northeast Normal University

Accurate and interpretable time series prediction is of great significance in production,it can help people deal with boiler abnormalities timely and accurately,and provide guarantee for safe *** paper proposes a time series prediction method based on multi-modality fuzzy cognitive maps with data preprocessing,which can soundly achieve the *** the model is for solving practical problems,the experiment in paper uses real boiler data to improve ***,the outliers in the dataset are replaced with reasonable data,then fuzzify the processed time ***,the subsequences are chosen in the time series,and the fuzzy cognitive maps model is established on each ***,the model outputs of each subsequence are mixed as the prediction results and obtaining the forecast *** experimental results contracted with Long Short-Term Memory(LSTM) show that the proposed model can well predict the changing trend of the actual industrial heat-conducting oil temperature of boiler.

关键词： fuzzy cognitive maps(FCMs) prediction of time series boiler heat-conducting oil temperature prediction time series completion

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WILD: a new in-the-Wild Image Linkage Dataset for synthetic image attribution

arXiv

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arXiv 2025年

作者： Bongini, Pietro Mandelli, Sara Montibeller, Andrea Casu, Mirko Pontorno, Orazio Ragaglia, Claudio Vittorio Zanchetta, Luca Aquilina, Mattia Wani, Taiba Majid Guarnera, Luca Tondi, Benedetta Boato, Giulia Bestagini, Paolo Amerini, Irene De Natale, Francesco Battiato, Sebastiano Barni, Mauro University of Siena Department of Information Engineering an Mathematics Italy Politecnico di Milano Department of Electronics Informatics and Bioengineering Italy University of Trento Department of Information Engineering and Computer Science Italy University of Catania Department of Mathematics and Computer Science Italy Sapienza University of Rome Departement of Computer Control and Management Engineering Italy

Synthetic image source attribution is an open challenge, with an increasing number of image generators being released yearly. The complexity and the sheer number of available generative techniques, as well as the scarcity of high-quality open source datasets of diverse nature for this task, make training and benchmarking synthetic image source attribution models very challenging. WILD1 is a new in-the-Wild Image Linkage Dataset designed to provide a powerful training and benchmarking tool for synthetic image attribution models. The dataset is built out of a closed set of 10 popular commercial generators, which constitutes the training base of attribution models, and an open set of 10 additional generators, simulating a real-world in-the-wild scenario. Each generator is represented by 1,000 images, for a total of 10,000 images in the closed set and 10,000 images in the open set. Half of the images are post-processed with a wide range of operators. WILD allows benchmarking attribution models in a wide range of tasks, including closed and open set identification and verification, and robust attribution with respect to post-processing and adversarial attacks. Models trained on WILD are expected to benefit from the challenging scenario represented by the dataset itself. Moreover, an assessment of seven baseline methodologies on closed and open set attribution is presented, including robustness tests with respect to post-processing. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep learning

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