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32nd ACM International Conference on Multimedia, MM 2024

作者： Li, Yudong Hou, Xianxu Dezhi, Zheng Shen, Linlin Zhao, Zhe School of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China School of AI and Advanced Computing Xi'an Jiaotong-Liverpool University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Tencent AI Lab Beijing China

ISBN: (纸本)9798400706868

While significant progress has been made in multi-modal learning driven by large-scale image-text datasets, there is still a noticeable gap in the availability of such datasets within the facial domain. To facilitate and advance the field of facial representation learning, we present FLIP-80M, a large-scale visual-linguistic dataset comprising over 80 million face images paired with text descriptions. FLIP-80M is constructed by leveraging the large openly available image-text-pair dataset LAION-5B and a mixed-method approach to filter face-related pairs from both visual and linguistic perspectives. Our curation process involves face detection, face caption classification, text de-noising, and synthesis-based image augmentation. As a result, FLIP-80M stands as the largest face-text dataset to date. To evaluate the potential of our dataset, we fine-tune the CLIP model using the proposed FLIP-80M, to create FLIP (Facial Language-Image Pretraining) and assess its representation capabilities across various downstream tasks. Our experiments demonstrate that our FLIP model achieves state-of-the-art results in a range of face analysis tasks, including face parsing, face alignment, and face attribute classification. The dataset and models are available at https://***/ydli-ai/FLIP. © 2024 ACM.

关键词： Face recognition

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Long‐time target tracking algorithm based on re‐detection multi‐feature fusion

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IET Cyber-Systems and Robotics 2022年第1期4卷 38-50页

作者： Junsuo Qu Chenxue Tang Yuan Zhang Kai Zhou Abolfazl Razi School of Automation Xi'an Key Laboratory of Advanced Control and Intelligent ProcessXi'an University of Posts and TelecommunicationsXi'anShaanxiChina School of Communication and Information Engineering Xi'an University of Posts and TelecommunicationsXi'anShaanxiChina School of Informatics Computing and Cyber SystemsNorthern Arizona UniversityFlagstaffAZUSA

This paper considers the problem of long-term target tracking in complex scenes when tracking failures are unavoidable due to illumination change,target deformation,scale change,motion blur,and other *** specifically,a target tracking algorithm,called re-detection multi-feature fusion,is proposed based on the fusion of scale-adaptive kernel correlation filtering and *** target tracking algorithm trains three kernel correlation filters based on the histogram of oriented gradients,colour name,and local binary pattern features and then obtains the fusion weight of response graphs corresponding to different features based on average peak correlation energy criterion and uses weighted average to complete the position estimation of the tracked *** order to deal with the problem that the target is occluded and disappears in the tracking process,a random fern classifier is trained to perform re-detection when the target is *** comparing the OTB-50 target tracking dataset,the experimental results show that the proposed tracker can track the target well in the occlusion attribute video sequence in the OTB-100 test dataset and has a certain improvement in tracking accuracy and success rate compared with the traditional correlation filter tracker.

关键词： machine learning pedestrian identification robustness visual surveillance visual tracking

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Direct fabrication of single-crystal-like structure using quasi-continuous-wave laser additive manufacturing

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Journal of Materials Science & Technology 2021年第1期60卷 216-221页

作者： Hui Xiao Manping Cheng Lijun Song State Key Laboratory of Advanced Design and Manufacturing for Vehicle Bodies Hunan UniversityChangsha 410082China Hunan Provincial Key Laboratory of Intelligent Laser Manufacturing Hunan UniversityChangsha 410082China School of Mechatronics Engineering Foshan UniversityFoshanGuangdong Province528001PR China

The synchronously periodic re-melting of molten pool was firstly introduced in additive manufacturing to promote the epitaxial growth of columnar structure using a novel quasi-continuous-wave(QCW)*** epitaxial growth of columnar structure was intensified and the single-crystal-like sample with highly oriented "zigzag" columnar grains was *** modified molten-pool geometry and the synchronously high-frequency re-melting of the molten pool contribute to the formation of singlecrystal-like *** work reports a new route to promote the continuously epitaxial growth of dendrites for fabrication of single-crystal-like sample.

关键词： Laser additive manufacturing Nickel alloys Solidification microstructure Texture Dendritic growth

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Hybrid Decision-making Strategy Combining Reinforcement Learning and Decision Rules for Highway Unmanned Driving

Hybrid Decision-making Strategy Combining Reinforcement Lear...

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IEEE International Conference on Unmanned Systems (ICUS)

作者： Lele Zhang Zeyu Yang Caocao Yu Ziniu Hu Yunshui Zhou Qingjia Cui State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle Hunan University Changsha China Wuxi Intelligent Control Research Institute of Hunan University Wuxi China

ISBN: (数字)9798350384185

ISBN: (纸本)9798350384192

In recent years, reinforcement learning has been employed to address the challenge of unmanned driving decision-making, due to its ability to handle uncertainty and continuous optimization. However, compared with rule-based algorithms, its training time is considerable and the reliability of its results is difficult to guarantee. To enhance the security of decision-making processes, this paper proposes a hybrid decision-making algorithm designed to accelerate the training process of reinforcement learning and enhance the reliability of decision-making. The method first evaluates the confidence levels of both rule-based and learning-based strategies by leveraging the data collected throughout the training phase. Subsequently, an activation function is crafted to determine the final strategy with the greatest level of confidence. This guarantees that the final strategy performs at least on par with the rule-based approach. A simulation focused on highway driving was conducted as a case study to illustrate the effectiveness of the proposed method. The results demonstrate that the proposed approach offers greater profitability than the rule-based method and higher reliability compared to the learning-based method.

关键词： Training Road transportation Learning systems Uncertainty Profitability Decision making Reinforcement learning Reliability engineering Security Optimization

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Family of Mutually Uncorrelated Codes for DNA Storage Address design

IEEE Transactions on Nanobioscience

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IEEE Transactions on Nanobioscience 2025年 PP卷 PP页

作者： Liu, Zhenlu Cao, Ben Shao, Qi Zheng, Yanfen Wang, Bin Zhou, Shihua Zheng, Pan Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China University of Canterbury Upper Riccarton Department of Accounting and Information Systems Christchurch8140 New Zealand

Deoxyribonucleic acid (DNA) has become an ideal medium for long-term storage and retrieval due to its extremely high storage density and long-term stability. But access efficiency is an existing bottleneck in DNA storage, especially the lack of high-quality random access address sequences. Therefore, in this paper, we report a series of approaches based on k-weakly mutually uncorrelated (k-WMU) codes to design the address sequence to improve the access efficiency of DNA storage. To address the problem of DNA sequences that are poorly scalable at the base level, we propose a 0-m-ruling coding scheme combined with k-WMU codes that can make address sequences avoid generating secondary structure with stem lengths ranging from 3 to 9. Based on the decoupled structure, We further extend the k-WMU codes with error correction function while satisfying combinatorial biological constraints. In order to investigate the performance of the designed address sequences for real-world applications, we perform simulation experiments based on thermodynamic properties and error correction capability as well as compared the minimum free energy (MFE), melting temperature (TM), and average decoding success rate (ADSR) with previous work. The results show that designed address sequences have a high MFE value and ADSR and a substantial reduction in TM-variance while satisfying the combinatorial biological constraints. As the quality of address sequences improves, this will help to achieve accurate random access as well as enhance the robustness of the DNA storage system. © 2002-2011 IEEE.

关键词： DNA sequences

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A Doctor Recommendation Framework for Online Medical Platforms Using Multi-Source Heterogeneous Data 2021

A Doctor Recommendation Framework for Online Medical Platfor...

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7th International Conference on computing and Artificial Intelligence, ICCAI 2021

作者： Che, Mingxuan Zhao, Lasheng Jin, Bo Dalian University Department of Information Engineering China Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University China School of Innovation and Entrepreneurship Dalian University of Technology China

ISBN: (纸本)9781450389501

The emergence of the online medical platform provides convenience for patients, but at the same time, how to choose the right doctor among thousands of doctors on the platform has become a problem for patients. Nowadays, most doctor recommendation methods meet computation inefficiency issues as the data amount is extreme huge. In addition, the data on different medical platforms cannot be utilized due to inconsistent structures. To address the above two problems, this paper proposes a framework for doctor recommendation using multi-source heterogeneous data with two models: knowledge fusion model and doctor recommendation model. In the knowledge fusion model, two systems are established for co-evolution to integrate multi-source heterogeneity data. One system maps various types of health data to coding strings for evolutionary calculation;the other uses co-evolution to generate a unified coding string. In the doctor recommendation model, the ranking of doctors under different attributes is used as input to obtain the comprehensive ranking of doctors, the method performs effective to fuse knowledge from all kinds of data. The experimental results on 1157 orthopedic surgeons show that our framework greatly improves the computational efficiency while maintaining the accuracy. © 2021 ACM.

关键词： Computational efficiency

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A Stable RTK/MEMS-IMU Tightly-Coupled Algorithm under Differential Corrections Outage

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IEEE Transactions on Vehicular Technology 2025年

作者： Bian, Yougang Cao, Shipeng Wang, Guangcai Qin, Xiaohui Hu, Manjiang Qin, Hongmao Hunan University State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle College of Mechanical and Vehicle Engineering Changsha410082 China Hunan University Wuxi Intelligent Control Research Institute Wuxi214115 China

The GNSS-RTK and MEMS-IMU integrated navigation system is a commonly used high-precision, low-cost solution for outdoor positioning. However, the significant measurement error of MEMS-IMU can rapidly lead to divergence in navigation state predictions based on inertial error propagation models. To overcome this limitation and enhance accuracy, maintaining the continuity of RTK measurement information is crucial. Nevertheless, due to the instability of wireless transmission links and communication equipment, or disruptions in base station observations, differential corrections often experience outages, resulting in a decline in measurement information quality and affecting the accuracy of integrated navigation. Therefore, this paper proposes a stable RTK/MEMS-IMU tightly coupled algorithm. During outages of differential corrections, the proposed algorithm utilizes historical differential corrections from the base station along with real-time rover station information to construct a stable double-difference carrier phase vector. This approach ensures the stability of measurement information and enhances positioning accuracy. Simulation results have verified the effectiveness of this method, and land tests have demonstrated that, compared to the traditional RTK/MEMS-IMU tightly coupled algorithm, the position and velocity accuracy of this algorithm during outages of differential corrections have been improved by 29.5% and 18.4%, respectively. © 1967-2012 IEEE.

关键词： Base stations

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Adaptive neural tracking control for nonlinear servo system with unknown time-varying parameters and uncertainties

Adaptive neural tracking control for nonlinear servo system ...

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第42届中国控制会议

作者： Faxiang Zhang Guanbin Gao Jing Na Jiande Wu Faculty of Mechanical and Electrical Engineering and Yunnan International Joint Laboratory of Intelligent Control and Application of Advanced Equipment Kunming University of Science and Technology Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University

This paper solves the neural network(NN) tracking control problem for uncertain nonlinear servo system with timevarying parameters and *** address the uncertain nonlinear function with unknown time-varying parameters(i.e.,unknown nonlinear spatiotemporal function),the time-varying parameter extraction method is used to separate the time-varying puameters from uncertain nonlinear spatiotemporal function,which yields an unknown state-based boundary *** the tools of NN and adaptive technology,an adaptive neural tracking controller is designed,which guarantees the uniformly ultimately bounded(UUB) performance of the resulting closed-loop *** effectiveness of the designed method is verified by simulations.

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Noise Detection and Removal for Underground Communication Systems Based on L2 N onnal Inner Product

Noise Detection and Removal for Underground Communication Sy...

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IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)

作者： Bangwei Yu Yong Wang Liangang Qi Yang Liu College of Information and Communication Engineering Harbin Engineering University Harbin China Key Laboratory of Advanced Ship Communication and Information Technology Harbin Engineering University Harbin China Sichuan Key Laboratory of Agile Intelligent Computing Chengdu China

ISBN: (数字)9798350389692

ISBN: (纸本)9798350389708

In underground communication systems, continuous mud pulse signals are susceptible to pump noise during transmission, resulting in a high bit error rate(BER). In this paper, a Normal Inner Product Orthogonal Matching Pursuit(NIPOMP) algorithm is proposed for the transmission characteristics of continuous waves in the underground. First, a cyclic prefix(CP) is added in front of the sent data. Then the observation vector is obtained by making a difference between the CP of the latter set of data and the CP of the former set of data. Then, the columns of the sensing matrix that are most correlated with the observation vectors are selected as candidate support sets by calculating the L2 norm. The least squares method was used to solve for the previous set of pump noise estimates. Finally, the pump noise is reconstructed using the correspondence between the time and frequency domains. This paper establishes a complete underground communication system. We discuss the noise characteristics of pump noise and analyze the denoising performance of the NIPOMP algorithm in depth. Simulation results show that the algorithm significantly reduces the system BER in complex channel environments with more drastic fading, including gas-phase, liquid-phase, gas-liquid two-phase, and liquid-solid two-channel environments.

关键词： Liquids Communication systems Simulation Oils Noise Noise reduction Matching pursuit algorithms Vectors Sensors Signal to noise ratio

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Trajectory Optimization and Control Method Based on Multistage Nonlinear Model Predictive Control 4

Trajectory Optimization and Control Method Based on Multista...

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4th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2023

作者： Zheng, Qingchun Peng, Zhi Zhu, Peihao Zhai, Ran Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control Tianjin China School of Mechanical Engineering Tianjin University of Technology Tianjin China Tianjin University of Technology National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin China

ISBN: (纸本)9798350314526

To improve the trajectory optimization efficiency of robot manipulators, this paper proposes a novel approach for trajectory optimization and control of manipulators based on multistage nonlinear model predictive control (msNLMPC). This approach is realized by using a neural state space model as the prediction model of nonlinear model predictive control (NLMPC). The simulation results show that the actual trajectory of the manipulator in our proposed scheme coincides with the desired trajectory. The manipulator achieves the control task of following the optimal trajectory. Our proposed method improves the trajectory optimization efficiency of the robot manipulator. © 2023 IEEE.

关键词： Model predictive control

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