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International Joint Conference on Neural Networks (IJCNN)

作者： Song Gong Kaijie Wu Zhiying Xia Lihua Ran Chaochen Gu Changsheng Lu Tongkun Guan Yudi Zhao Department of Automation Shanghai Jiao Tong University Shanghai China Laboratory of Marine Ecosystem and Biogeochemistry State Oceanic Administration Hangzhou China Department of Engineering and Computer Science The Australian National University Canberra Australian

Automatic diatom detection refers to the task of identifying and characterizing diatoms based on artificial intelligence. It will replace traditional time-consuming and laborious manual microscopy method of diatom observation to greatly accelerate the process of diatom research and some diatomrelated studies, such as diatom abundance statistics, using diatom properties for environmental monitoring and paleoenvironmental reconstruction. However, complex background interference and the detection of slender diatoms with different integrity are two major challenges for automatic diatom detection. To solve the mentioned-above issues, we propose an oriented object detector for automatic diatom detection based on RepPoints, called OOD-RepPoints. Specifically, for encouraging the network to adaptively capture the feature of slender diatoms, we design a cascaded feature refinement head (CFRH) which consists of points generation stage and points refinement stage, to progressively optimize the extraction of slender diatom features. Furthermore, to fit the shape of diatoms well, especially for slender diatoms, we propose a tailored label assignment strategy for our CFRH, which contains a short side assigner (SSA) for points generation stage and an adaptive IoU thresholds assigner (AITA) for points refinement stage. Besides, we contribute a so called O-Diatom dataset for automatic diatom detection. The dataset has 1711 images which contains 3949 diatoms and provides finely manual oriented bounding box annotations. Extensive experiments demonstrated our method achieve state of the art performance and can reach mAP 89.9% which is highest on O-Diatom, and shows competitive results on slender categories of publicly available datasets (i.e., DOTA and HRSC2016).

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Transformer-based Single-Cell Language Model: A Survey

arXiv

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arXiv 2024年

作者： Lan, Wei He, Guohang Liu, Mingyang Chen, Qingfeng Cao, Junyue Peng, Wei The Guangxi Key Laboratory of Multimedia Communications and Network Technology School of Computer Electronic and Information Guangxi University Nanning China The School of Computer and Electronic Information Guangxi University Nanning China The School of Computer Electronic and Information Guangxi University Nanning China The College of Life Science and Technology Guangxi University Nanning China The Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China

The transformers have achieved significant accomplishments in the natural language processing as its outstanding parallel processing capabilities and highly flexible attention mechanism. In addition, increasing studies based on transformers have been proposed to model single-cell data. In this review, we attempt to systematically summarize the single-cell language models and applications based on transformers. First, we provide a detailed introduction about the structure and principles of transformers. Then, we review the single-cell language models and large language models for single-cell data analysis. Moreover, we explore the datasets and applications of single-cell language models in downstream tasks such as batch correction, cell clustering, cell type annotation, gene regulatory network inference and perturbation response. Further, we discuss the challenges of single-cell language models and provide promising research directions. We hope this review will serve as an up-to-date reference for researchers interested in the direction of single-cell language models. Copyright © 2024, The Authors. All rights reserved.

关键词： Cytology

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Exploring the Economic Feasibility of Advanced Air Mobility in the Early Stages

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第5期9卷 4826-4830页

作者： Jingqiu Guo Long Chen Lingxi Li Xiaoxiang Na Ljubo Vlacic Fei-Yue Wang Key Laboratory of Road and Traffic Engineering of the Ministry of Education Tongji University Shanghai China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Department of Electrical and Computer Engineering Purdue School of Engineering and Technology Indiana University–Purdue University Indianapolis (IUPUI) Indianapolis IN USA Department of Engineering University of Cambridge Cambridge U.K. Institute of Intelligent and Integrated Systems and the School of Engineering and Built Environment Griffith University Nathan QLD Australia State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Institute of Engineering Macau University of Science and Technology Macau China

Advanced Air Mobility (AAM) envisages a sustainable, safe, convenient, and affordable air transport system. In socio-technical transition of AAM, there are a number of trade-offs in ecosystem that need to be studied. Three perspectives on economic feasibility are explored: first, based on history of VTOL services and value of time estimates, we discuss whether AAM can provide customers with competitive mobility services; second, what are the stakeholders’ insights on the deployment of AAM; last, the experience in the development of autonomous driving technology, such as parallel intelligence, can inform future AAM research.

关键词： Active appearance model Costs Economics Transportation Helicopters Aircraft propulsion Public transportation

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Safe Reinforcement Learning for Social Motor Coordination in Two Dimensional Space

Safe Reinforcement Learning for Social Motor Coordination in...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Fan Yang Yuening Huang Chao Zhai Hehong Zhang China University of Geosciences Wuhan Campus School of Automation Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Rice University George R. Brown School of Engineering and Computing Houston Texas United States Fuzhou University School of Computer and Data Science Fuzhou China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

This paper presents an interactive motion control method based on reinforcement learning, designed to assist children with autism who have social motor impairments through a mirror game intervention. The virtual teacher uses the Deep Deterministic Policy Gradient (DDPG) algorithm to optimize its actions, guiding the participant to follow a Lissajous trajectory. To ensure safety, a motion-correction mechanism was developed, which automatically adjusts actions when the predicted trajectory surpasses predefined safety boundaries. The reward function considers both the distance between the virtual teacher and the target trajectory, as well as the distance between the virtual teacher and the participant, with dynamic adjustments applied by the motion-correction mechanism. Experimental results demonstrate that the virtual teacher effectively guides the participant towards the target trajectory while adhering to safety constraints.

关键词： Autism Filters Reinforcement learning Games Variable speed drives Motors Trajectory Safety Mirrors Motion control

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FreestyleRet: Retrieving Images from Style-Diversified Queries

arXiv

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arXiv 2023年

作者： Li, Hao Jia, Curise Jin, Peng Cheng, Zesen Li, Kehan Sui, Jialu Liu, Chang Yuan, Li School of Electronic and Computer Engineering Peking University Shenzhen Graduate School China Peng Cheng Laboratory Shenzhen China Department of Automation and BNRist Tsinghua University China School of Science and Engineering Chinese University of Hong Kong Shenzhen China

Image Retrieval aims to retrieve corresponding images based on a given query. In application scenarios, users intend to express their retrieval intent through various query styles. However, current retrieval tasks predominantly focus on text-query retrieval exploration, leading to limited retrieval query options and potential ambiguity or bias in user intention. In this paper, we propose the Style-Diversified Query-Based Image Retrieval task, which enables retrieval based on various query styles. To facilitate the novel setting, we propose the first Diverse-Style Retrieval dataset, encompassing diverse query styles including text, sketch, low-resolution, and art. We also propose a light-weighted style-diversified retrieval framework. For various query style inputs, we apply the Gram Matrix to extract the query’s textural features and cluster them into a style space with style-specific bases. Then we employ the style-init prompt tuning module to enable the visual encoder to comprehend the texture and style information of the query. Experiments demonstrate that our model, employing the style-init prompt tuning strategy, outperforms existing retrieval models on the style-diversified retrieval task. Moreover, style-diversified queries (sketch+text, art+text, etc) can be simultaneously retrieved in our model. The auxiliary information from other queries enhances the retrieval performance within the respective query1. The code is available in https: //***/CuriseJia/FreeStyleRet. © 2023, CC BY.

关键词： Textures

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Emergency Scheduling of Aerial Vehicles via Graph Neural Neighborhood Search

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Guo, Tong Mei, Yi Du, Wenbo Lv, Yisheng Li, Yumeng Song, Tao Beihang University State Key Laboratory of CNS/ATM School of Electronic and Information Engineering Beijing100191 China Victoria University of Wellington Centre for Data Science and Artificial Intelligence & School of Engineering and Computer Science Wellington New Zealand Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China Jinan250353 China University of the Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Beijing Institute of Technology School of Aerospace Engineering Beijing China

The thriving advances in autonomous vehicles and aviation have enabled the efficient implementation of aerial last-mile delivery services to meet the pressing demand for urgent relief supply distribution. Variable Neighborhood Search (VNS) is a promising technique for aerial emergency scheduling. However, the existing VNS methods usually exhaustively explore all considered neighborhoods with a prefixed order, leading to an inefficient search process and slow convergence speed. To address this issue, this paper proposes a novel graph neural neighborhood search algorithm, which includes an online reinforcement learning (RL) agent that guides the search process by selecting the most appropriate low-level local search operators based on the search state. We develop a dual-graph neural representation learning method to extract comprehensive and informative feature representations from the search state. Besides, we propose a reward-shaping policy learning method to address the decaying reward issue along the search process. Extensive experiments conducted across various benchmark instances demonstrate that the proposed algorithm significantly outperforms the state-of-the-art approaches. Further investigations validate the effectiveness of the newly designed knowledge guidance scheme and the learned feature representations. © 2020 IEEE.

关键词： Reinforcement learning

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Local Neighbor Propagation on Graphs for Robust Feature Matching

SSRN

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SSRN 2023年

作者： Guo, Hanlin Xiao, Guobao Su, Lumei Zhou, Jiaxing Wang, Dahan Xiamen Key Laboratory of Frontier Electric Power Equipment and Intelligent Control School of Electrical Engineering and Automation Xiamen University of Technology China Fujian Key Laboratory of Sensing and Computing for Smart Cities School of Information Science and Engineering Xiamen University China College of Computer and Control Engineering Minjiang University China Fujian Key Laboratory of Pattern Recognition and Image Understanding School of Computer and Information Engineering Xiamen University of Technology China

Establishing reliable correspondences between two sets of feature points is a critical preprocessing step in many computer vision and pattern recognition tasks. In this paper, we propose a novel robust Local Neighbor Propagation on Graphs based feature matching (LNPG) method, to obtain good correspondences for feature matching. LNPG starts from a novel neighborhood graph construction strategy. The strategy leverages the spatial consistency constraint to generate a series of neighborhood sets, and employs the residual information to preserve the local neighborhood relationships of potential inliers (i.e., true matches). Subsequently, LNPG incorporates local neighbor propagation into the graph to enhance connections between data in different neighborhoods, by using the path-based similarity measurement and the adaptive graph partition. In addition, LNPG includes a novel consistency-filtering-based clustering algorithm for robust feature matching. This clustering algorithm introduces a reliable neighborhood consistency measure function and an effective cluster merging criterion for cluster detection and cluster merging during the clustering process, respectively. Overall, LNPG not only effectively distinguishes inliers from outliers, but also reliably classifies inliers into different transformation models between pairs of images. Experiments on publicly available datasets with different types of image transformations show the superiority of our LNPG in comparison with other state-of-the-art methods. © 2023, The Authors. All rights reserved.

关键词： Clustering algorithms

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Adaptive Interacting Multiple Model for Underwater Maneuvering Target Tracking with One-Step Randomly Delayed Measurements

SSRN

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SSRN 2023年

作者： Li, Xiaohua Lu, Bo Li, Yuxing Lu, Xiaofeng Jin, Haiyan School of Computer Science and Engineering Xi'an University of Technology Xi’an710048 China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an710048 China School of Automation and Information Engineering Xi'an University of Technology Xi’an710048 China

The existing interacting multiple model (IMM) suffers from two problems when tracking an underwater maneuvering target: 1) the conventional nonlinear filters in IMM cannot work on the condition of delayed measurement, 2) the inefficiency of model switching in IMM may degrade the tracking performance. In order to handle the delayed measurement, an augmented state vector including target state and measurement noise is designed. Thus, under two Gaussian assumptions, the estimation of the target state can be divided into two steps: measurement noise filter and target state filter. Then, the new extended Klaman filter (EKF) and unscented Kalman filter (UKF) with delayed measurement are implemented as two variations of the proposed filter. In addition, to solve the problem of low switching efficiency between IMM models, a transfer probability corrected function is designed to adaptively update the transfer probability matrix (TPM) in IMM. The improved TPM can make full use of the current and past model information. The simulation results show that the proposed algorithm outperforms the existing IMM-EKF and IMM-UKF. © 2023, The Authors. All rights reserved.

关键词： Stochastic systems

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Attention Mechanism-Based Multilayer Pruned Graph Convolutional Network for Industrial Fault Diagnosis

SSRN

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SSRN 2024年

作者： Zhao, Qiang Chen, Libin Chen, Qiyue Han, Yinghua Sun, Jie College of Information Science and Engineering Northeastern University Shenyang China School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao China School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao China The State Key Laboratory of Rolling and Automation Northeastern University United States Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China

Fault diagnosis in complex industrial processes has become a vital issue drawing the attention in the industrial field. With the increasing deployment of sensors in industrial process equipment, deep learning (DL) based approaches have become more feasible. However, most existing DL-based approaches have two main drawbacks. Firstly, there is complex coupling between variables in industrial processes. Most existing approaches have only one metric to build the network, which cannot fully explore the potential relationships between variables. Secondly, there are few studies on aggregating critical features of different variables and reducing the interference of irrelevant and redundant information. To overcome these challenges, this paper presents a new approach for industrial process troubleshooting, called the variational modal decomposition-multi-head graph pruning network (VMD-MHGPN). This approach draws inspiration from the attention mechanism and combines it with graph construction and pruning. Specifically, we use multiple attention mechanisms to learn the interactions between nodes. Previously, variational modal decomposition (VMD) is applied to extract fault features at different time scales and use them as subsequent graph nodes. To reduce the impact of irrelevant and redundant nodes on the fault feature representation, we introduce the graph pruning operation, which uses an attention mechanism to score nodes and discard relatively unimportant ones. The final representation of fault features is generated by computing the weights of different interaction graphs based on the attention mechanism and aggregating multiple interaction graphs according to the weights. The proposed approach for diagnosing faults in industrial processes proved efficient and superior based on experimental findings from a three-phase flow apparatus and a Tennessee Eastman process. © 2024, The Authors. All rights reserved.

关键词： Failure analysis

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Signal Detection Method Based on Data Characteristics in Vehicular Ad Hoc Networks

Signal Detection Method Based on Data Characteristics in Veh...

引用

IEEE Symposium on Intelligent Vehicle

作者： Yi Li Shuangshuang Han Shichao Chen School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Key Laboratory of Ocean Observation Technology MNR Tianjin China State Key Laboratory of Multimodal Artiffcial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing Huairou Academy of Pararllel Sensing Beijing China

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

In the area of vehicular ad hoc networks (VANETs), efficient data transmission stands as a cornerstone for ensuring dependable communication. This paper focuses on the key role of optimizing antenna configuration concerning data volumes to improve communication efficiency within VANETs. To attain efficient data transmission and improve user experience, we analyze and compare the influence of varied data volumes and antenna settings on communication quality stemming from bit error rate performance. Through experimental validation across diverse signal detection scenarios, disparities in performance across different antenna configurations are confirmed, thereby furnishing invaluable insights into the efficacy of dynamically selecting antenna configurations based on data volumes. In essence, this paper aims to demonstrate the intricacies of optimizing antenna settings to adaptly navigate fluctuations in transmitted data volumes, thereby increasing communication efficiency and ensuring robust communication in VANETs.

关键词： Navigation Transmitting antennas Bit error rate Vehicular ad hoc networks Rician channels Reliability engineering User experience

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