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SSRN 2022年

作者： Fan, Chao Chen, Zhixiang Lin, Hao Wang, Xiao School of Artificial Intelligence and Big Data Henan University of Technology Henan Province Zhengzhou City China School of Information Science and Engineering Henan University of Technology Henan Province Zhengzhou City China Key Laboratory of Grain Information Processing and Control Ministry of Education Henan Province Zhengzhou City China

We provide an end-to-end unsupervised training model called TCGFusion: a network for PET-MRI fusion based on GAN and transformer. We set the generator to a dual-path layout to assure the gathering of both global and detailed image information. To guarantee the coarse scale picture extraction and prevent the high computational cost, the global path T-Path utilizes Grid-former (a residual grid transformer structure). The detailed path C-Path is DenseNet, and its multi-cascade structure captures image details well. Additionally, we set up a dual adversarial mechanism between the fused image and the source image, creating a dual discriminator to determine the distribution of structural similarity between the fused result and the source image, and using patch-SSIM to reinforce the semantic constraints between them and promote the image details. According to our extensive studies on public datasets, medical image fusion demonstrate the superiority of our TCGFusion compared to the state-of-the-art image fusion algorithms. © 2022, The Authors. All rights reserved.

关键词： Semantics

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A Multi-Stage Goal-Driven Network for Pedestrian Trajectory Prediction

arXiv

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arXiv 2024年

作者： Wu, Xiuen Wang, Tao Cai, Yuanzheng Liang, Lingyu Papageorgiou, George Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China College of Computer and Data Science Fuzhou University Fuzhou China School of Electronic and Information Engineering South China University of Technology Guangzhou China SYSTEMA Research Center European University Cyprus Nicosia Cyprus

Pedestrian trajectory prediction plays a pivotal role in ensuring the safety and efficiency of various applications, including autonomous vehicles and traffic management systems. This paper proposes a novel method for pedestrian trajectory prediction, called multi-stage goal-driven network (MGNet). Diverging from prior approaches relying on stepwise recursive prediction and the singular forecasting of a long-term goal, MGNet directs trajectory generation by forecasting intermediate stage goals, thereby reducing prediction errors. The network comprises three main components: a conditional variational autoencoder (CVAE), an attention module, and a multi-stage goal evaluator. Trajectories are encoded using conditional variational autoencoders to acquire knowledge about the approximate distribution of pedestrians' future trajectories, and combined with an attention mechanism to capture the temporal dependency between trajectory sequences. The pivotal module is the multistage goal evaluator, which utilizes the encoded feature vectors to predict intermediate goals, effectively minimizing cumulative errors in the recursive inference process. The effectiveness of MGNet is demonstrated through comprehensive experiments on the JAAD and PIE datasets. Comparative evaluations against state-of-the-art algorithms reveal significant performance improvements achieved by our proposed method. Copyright © 2024, The Authors. All rights reserved.

关键词： Trajectories

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FedCross: Intertemporal Federated Learning Under Evolutionary Games

arXiv

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arXiv 2024年

作者： Lu, Jianfeng Zhang, Ying Jia, Riheng Cao, Shuqin Liu, Jing Fu, Hao School of Computer Science and Technology Wuhan University of Science and Technology China Key Laboratory of Social Computing and Cognitive Intelligence Dalian University of Technology Ministry of Education China School of Computer Science and Technology Zhejiang Normal University China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology China

Federated Learning (FL) mitigates privacy leakage in decentralized machine learning by allowing multiple clients to train collaboratively locally. However, dynamic mobile networks with high mobility, intermittent connectivity, and bandwidth limitation severely hinder model updates to the cloud server. Although previous studies have typically addressed user mobility issue through task reassignment or predictive modeling, frequent migrations may result in high communication overhead. Addressing this challenge involves not only dealing with resource constraints, but also finding ways to mitigate the challenges posed by user migrations. We therefore propose an intertemporal incentive framework, FedCross, which ensures the continuity of FL tasks by migrating interrupted training tasks to feasible mobile devices. Specifically, FedCross comprises two distinct stages. In Stage 1, we address the task allocation problem across regions under resource constraints by employing a multi-objective migration algorithm to quantify the optimal task receivers. Moreover, we adopt evolutionary game theory to capture the dynamic decision-making of users, forecasting the evolution of user proportions across different regions to mitigate frequent migrations. In Stage 2, we utilize a procurement auction mechanism to allocate rewards among base stations, ensuring that those providing high-quality models receive optimal compensation. This approach incentivizes sustained user participation, thereby ensuring the overall feasibility of FedCross. Finally, experimental results validate the theoretical soundness of FedCross and demonstrate its significant reduction in communication overhead. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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Research on Identifiability of Equivalent Motor in Composite Load Model

Research on Identifiability of Equivalent Motor in Composite...

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Power Tech Conference

作者： Ma Jin Han Dong He Renmu Dong Zhaoyang David J. Hill Key Laboratory of Power System Protection and Dynamic Security Monitoring and Control of Ministry of Education North China Electric Power University Beijing China School of Information Technology and the Electrical Engineering University of Queensland Saint Lucia QLD Australia Department of Information Engineering Australian National University Australia

Load modeling is very important for power system dynamic analysis and control. The composite load model widely applied recently in the power system operation centre consists of the static load and the equivalent motor. Current practices in measurement-based load modeling identify all the parameters in this composite load model. However, it is not clear whether all these parameters could be identified from the measurements. This paper investigates the identifiability of the equivalent motor parameters in the composite load model. Trajectory sensitivity approach is applied first to find the motor parameters that have great effects on the measured active as well as reactive load dynamics. The analysis results show that the motor outputs have various sensitivities with respect to the parameters. Since the voltage disturbance, the active load and the reactive load dynamics are applied to identify the motor parameters, those parameters affecting the measurements to a great extent are observable, thus identifiable from the measurements; while those that have little effects on the motor outputs are unobservable from the measurements and consequently unidentifiable. The case studies verify the identifiability of the motor parameters.

关键词： Load modeling Power system modeling Power system dynamics Power system simulation Power system stability Power system analysis computing Power system measurements Voltage Power system control Power systems

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Platoon Keeping and Collision Avoidance Based on Distributed Model Predictive control Under Arbitrary Communication Chain Malfunction

Platoon Keeping and Collision Avoidance Based on Distributed...

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Chinese Automation Congress (CAC)

作者： Nan Chen Yutao Chen Dingci Lin Jie Huang College of Electrical Engineering and Automation Fuzhou University Fuzhou China 5G+Industrial Internet Institute Fuzhou University Fuzhou China Education Department of Fujian Province Key Laboratory of Industrial Automation Control Technology and Information Processing Fuzhou China

In this paper, a platoon keeping and collision avoidance method based on distributed model predictive control(DMPC) is proposed to solve the problem of arbitrary communication chain malfunctions under a bidirectional-leader topology. Firstly, for the platoon under normal communication, a DMPC controller is designed by using the trajectory information of the leader and predecessor vehicle, which also satisfies the situation when communication chain between the follower (except follower 1) and the leader malfunction. Secondly, for other communication chain malfunction situation, we proposed the corresponding communication topology transformation scheme and controller transformation scheme, which makes the proposed control scheme can adapt to arbitrary communication chain malfunction situation. Finally, as for the control strategy proposed in this paper, some simulation experiments are carried out.

关键词： Adaptation models Automation Predictive models Topology Trajectory Collision avoidance Predictive control

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Size-Invariance Matters: Rethinking Metrics and Losses for Imbalanced Multi-object Salient Object Detection

arXiv

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arXiv 2024年

作者： Li, Feiran Xu, Qianqian Bao, Shilong Yang, Zhiyong Cong, Runmin Cao, Xiaochun Huang, Qingming Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Science Beijing Jiaotong University Beijing China School of Control Science and Engineering Shandong University Jinan China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Cyber Science and Tech. Sun Yat-Sen University Shenzhen Campus China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We observe that current metrics are size-sensitive, where larger objects are focused, and smaller ones tend to be ignored. We argue that the evaluation should be size-invariant because bias based on size is unjustified without additional semantic information. In pursuit of this, we propose a generic approach that evaluates each salient object separately and then combines the results, effectively alleviating the imbalance. We further develop an optimization framework tailored to this goal, achieving considerable improvements in detecting objects of different sizes. Theoretically, we provide evidence supporting the validity of our new metrics and present the generalization analysis of SOD. Extensive experiments demonstrate the effectiveness of our method. The code is available at https://***/Ferry-Li/SI-SOD. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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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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Distributed Differential Game for Formation control of Multi-UAV with Obstacle Avoidance

Distributed Differential Game for Formation Control of Multi...

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Chinese Automation Congress (CAC)

作者： Zeyi Li Wenyan Xue Dongfang Li Dingci Lin Jie Huang College of Electrical Engineering and Automation Fuzhou University Fuzhou China 5G+Industrial Internet Institute Fuzhou University Fuzhou China Education Department of Fujian Province Key Laboratory of Industrial Automation Control Technology and Information Processing Fuzhou China

For the multi-unmanned aerial vehicle formation control (MUAVFC) in obstacle environments, this work proposes a new distributed non-cooperative differential game framework. This scheme adequately reflects the dynamic interaction of each UAV in the formation control process and effectively reflects the interaction of individual strategies and behaviors. Meanwhile, the game model is added with obstacle avoidance penalty constraints to achieve obstacle avoidance in the UAV formation flight and ensure flight safety. Each UAV needs to obtain the information of its neighbors following the communication topology to minimize the cost function, so that all strategy converges to Nash equilibrium. Based on the existing differential game method that only considers the formation goal, this work adds a penalty function term representing the distance between obstacles in the game model to achieve flight obstacle avoidance. In addition, this work strictly proves the existence of Nash equilibrium. The simulation results verify that the UAVs in this scheme have successfully formed a formation.

关键词： Simulation Differential games Games Autonomous aerial vehicles Nash equilibrium Formation control Safety

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5G ENTERING INTO A NEW ERA

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China Communications 2017年第12期14卷 I0002-I0004页

作者： Feifei Gao Changle Li Jun Fang Kaizhi Huang Zhi Quan Chengwen Xing Jianhua Zhang Zhongshan Zhang Liqiang Zhao Yulong Zou Department of Automation Tsinqhua UniversityBeiiingChina the State Key Laboratory of Integrated Services Networks Xidian University china communications ieee communications magazine ieee signal processing letters national digital switching system engineering&technological research center the college of information engineering shenzhen university the school of information and electronics beijing institute of technology bupt itu-r imt-2020 channel model ieee comm the school of computer and communication engineering in the university of science and technology beijing(ustb) state key lab.of integrated ser vice networks xidian university the nanjing university of posts and telecommunications(nupt)

The ever increasing wireless data services, such asimaging, video, audio, multimedia, etc., have de-mands for the very high speed wireless communicationsand network, which are unfortunately a bottleneck whencombining with the wireline core network. Users＇ arenow expecting high quality of experience with low-costdevices, ubiquitous connectivity, energy efficiency, highreliability, or even ultra-low latency if a vehicle terminalis applied.

关键词： 5G ENTERING A NEW ERA

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Dfe-Net: Dual-Branch Feature Extraction Network for Enhanced Segmentation in Skin Lesion

SSRN

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SSRN 2022年

作者： Fan, Chao Yang, Litao Lin, Hao Qiu, Yingying School of Artificial Intelligence and Big Data Henan University of Technology Henan Province Zhengzhou City China Key Laboratory of Grain Information Processing and Control Ministry of Education Henan Province Zhengzhou City China School of Information Science and Engineering Henan University of Technology Henan Province Zhengzhou City China

Skin lesion segmentation is a critical method for extracting pathological information from dermoscopy images, which is of great significance for lesion location, recognition, monitoring and treatment. Due to the varied sizes of lesions, different forms and colors, and blurring boundaries, the present detection methods are unable to accurately anticipate local features, which has an impact on the segmentation accuracy. Therefore, the Dual-branch Feature Extraction Network (DFE-Net) is suggested in this paper. We designed two types of encoders based on the Transformer and Efficient Channel Attention (ECA) module, one for extracting the global feature and another for the local feature, before fusing and decoding. Simultaneously, the skip connections from the Enhanced ECA Feature Extraction Modules to decoders are designed to reduce feature loss during decoding, this will restore the boundaries and local features to the greatest extent possible. We test the model on three publicly available skin lesion datasets: PH2, ISIC-2016, and ISIC-2018. The results show that our model is superior to other previous methods, and the segmentation characteristics are more accurate representations of the actual lesion. © 2022, The Authors. All rights reserved.

关键词： Feature extraction

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