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

作者： Sixuan Xing Jianhong Xiang Ying Wang Liangang Qi Yu Zhong 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 Agile and Intelligent Computing Key Laboratory Chengdu China

ISBN: (数字)9798350389692

ISBN: (纸本)9798350389708

Filter bank multicarrier technique (FBMC) is considered as a promising modulation technique suitable for future communications. However, the presence of intrinsic interference prevents the efficient implementation of techniques such as channel estimation. This paper presents a encode double auxiliary pilot channel estimation method (EDAP) for filter bank multicarrier offset quadrature amplitude modulation (FBMC/OQAM) systems. In order to eliminate the intrinsic interference, we add auxiliary symbols around the pilot, and at the same time perform a precoding process to minimize the auxiliary pilot energy loss and computational complexity. In order to validate the above method, the OQAM/FBMC system is modeled. Theoretical analysis and numerical results show that the method improves the channel estimation accuracy by 2.15-3.22 dB in double selective channels while maintaining low power consumption and low complexity.

关键词： Power demand Spectral efficiency Quadrature amplitude modulation System performance Precoding Channel estimation Filter banks Symbols Filtering algorithms Numerical models

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Exploring the Reform of Mechanical Drawing Teaching and Undergraduate Training Based on the 'Integration of Competition-Oriented Education and Industry-University Collaboration' 13

Exploring the Reform of Mechanical Drawing Teaching and Unde...

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13th International Conference on Educational and Information Technology, ICEIT 2024

作者： Runfeng, Zhang Yankun, Ming Zhenzhong, Liu Jianguo, Wu Xiaofei, Wang Yanfang, Sun School of Mechanical Engineering Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control Tianjin300384 China Tianjin University of Technology National Demonstration Center for Experimental Mechanical and Electrical Engineering Education China Tianjin Hanhailanfan Marine Technology Co. Ltd. Tianjin300457 China

ISBN: (纸本)9798350372663

This study aims to explore the reform of mechanical drawing teaching and the undergraduate training model with the goal of cultivating high-quality mechanical engineering undergraduate students. Grounded in the concepts of 'synergy between science and education' and 'integration of competition-oriented education and industry-university collaboration,' this research places talent cultivation as the central focus, linking it with discipline construction and emphasizing scientific research. The study promotes the reform of a collaborative education mechanism known as 'integration of competition-oriented education and industry-university collaboration,' effectively establishing a multi-level and multi-dimensional undergraduate training model for mechanical engineering students that incorporates the 'dual mentor system' and integrates theory, practice, and application. To apply this model in teaching practice, this research has undertaken the reform of traditional engineering drawing education, specifically addressing the issue of the gap between theory and practice in conventional teaching approaches. The proposed training program not only facilitates the development of innovative mechanical engineering professionals but also offers insights for the reform of undergraduate teaching models in the context of constructing 'new engineering disciplines'. © 2024 IEEE.

关键词： Competition

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Exploration and Practice of Cultivating Methods of Undergraduates' Ability to Apply Knowledge: Taking Engineering Drawing course as an example 23

Exploration and Practice of Cultivating Methods of Undergrad...

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8th International Conference on Distance Education and Learning, ICDEL 2023

作者： Wang, Xiaofei Wang, Jutao Zhang, Runfeng Yuan, Jingyao Tianjin Key Laboratory of the Design and Intelligent Control of the Advanced Mechatronical System Natl. Demonstration Ctr. for Exp. Mech. and Elec. Eng. Education Tianjin University of Technology Tianjin University of Technolog China Tianjin University of Technology China

ISBN: (纸本)9798400700422

The ability to apply knowledge is one of the most important abilities for university students. In the paper, it introduces that how to foster the ability in the teaching of engineering drawing course, and presents some views about and exploration of the reform of the engineering drawing course for cultivating engineers with modern consciousness and innovation quality. In accordance with the standards of engineering education accreditation, based on the idea of Conceive-design-Implement-Operate (CDIO), ideological and political of curriculum is merged into the course. The course reform better improve the quality of engineering education, lay a solid foundation of the specialty, and cultivate students' spatial thinking ability. At the same time, the course reform help students have a more comprehensive understanding to product-formation process, and then enhance students' sense of macro control of products. Combined with the characteristic of the engineering education accreditation and the CDIO in the teaching syllabus, curriculum structure, teaching methods and experiments are reformed. Following the pace of technological development, starting from stimulating students' interest in learning, integrating a 3D printing project into the teaching reform of engineering drawing is explored. There are two different level of introducing 3D printing into the course. They are shallow introduction and deep participation in producing a product by 3D printing. In the teaching mode of deep integration, students participate in the whole process of product design, modeling, 3D printing and all other 3D printing links. The implementation of this model is conducive to arousing students' interest in learning, giving free reign to their subjective initiative, enabling students to enjoy the learning process. Finally, the teaching objectives are realized, and their innovative ability are improved. © 2023 ACM.

关键词： Curricula

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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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RobNODDI: Robust NODDI Parameter Estimation with Adaptive Sampling Under Continuous Representation 15th

RobNODDI: Robust NODDI Parameter Estimation with Adaptive S...

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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2024

作者： Xiao, Taohui Cheng, Jian Fan, Wenxin Yang, Jing Li, Cheng Dong, Enqing Wang, Shanshan School of Mechanical Electrical and Information Engineering Shandong University Weihai264209 China State Key Laboratory of Software Development Environment Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing Institute of International Innovation Beihang University Zhejiang Hangzhou China Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China Peng Cheng Laboratory Guangdong Shenzhen China

ISBN: (纸本)9783031869198

Neurite Orientation Dispersion and Density Imaging (NODDI) is an important imaging technology used to evaluate the microstructure of brain tissue, which is of great significance for the discovery and treatment of various neurological diseases. Current deep learning-based methods perform parameter estimation through diffusion magnetic resonance imaging (dMRI) with a small number of diffusion gradients. These methods speed up parameter estimation and improve accuracy. However, the diffusion directions used by most existing deep learning models during testing needs to be strictly consistent with the diffusion directions during training. This results in poor generalization and robustness of deep learning models in dMRI parameter estimation. In this work, we first verify that the parameter estimation performance of current mainstream methods will significantly decrease when the testing diffusion directions and the training diffusion directions are inconsistent. A robust NODDI parameter estimation method with adaptive sampling under continuous representation (RobNODDI) is proposed. Furthermore, long short-term memory (LSTM) units and fully connected layers are selected to learn continuous representation signals. To this end, we use a total of 100 subjects to conduct experiments based on the Human Connectome Project (HCP) dataset, of which 60 are used for training, 20 are used for validation, and 20 are used for testing. The test results indicate that RobNODDI improves the generalization performance and robustness of the deep learning model, enhancing the stability and flexibility of deep learning NODDI parameter estimatimation applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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FLIP-80M: 80 Million Visual-Linguistic Pairs for Facial Language-Image Pre-Training 24

FLIP-80M: 80 Million Visual-Linguistic Pairs for Facial Lang...

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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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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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Pre-Equalization Aided Grant-Free Massive Access in Massive MIMO System

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IEEE Transactions on Vehicular Technology 2025年第6期74卷 9979-9984页

作者： Wang, Yueqing Mei, Yikun Gao, Zhen Wan, Ziwei Ning, Boyu Mi, De Muhaidat, Sami School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology MIIT Key Laboratory of Complex-Field Intelligent Sensing Beijing100081 China Yangtze Delta Region Academy Jiaxing314019 China Beijing Institute of Technology Advanced Technology Research Institute Jinan250307 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China Birmingham City University School of Computing and Digital Technology BirminghamB55JU United Kingdom Khalifa University KU 6 G Research Center Department of Computer and Information Engineering Abu Dhabi United Arab Emirates

The spatial diversity and multiplexing advantages of massive multi-input-multi-output (mMIMO) can significantly improve the capacity of massive non-orthogonal multiple access (NOMA) in machine type communications. However, state-of-the-art grant-free massive NOMA schemes for mMIMO systems require accurate estimation of random access channels to perform activity detection and the following coherent data demodulation, which suffers from excessive pilot overhead and access latency. To address this, we propose a pre-equalization aided grant-free massive access scheme for mMIMO systems, where an iterative detection scheme is conceived. Specifically, the base station (BS) firstly activates one of its antennas (i.e., beacon antenna) to broadcast a beacon signal, which facilitates the user equipment (UEs) to perform downlink channel estimation and pre-equalize the uplink random access signal with respect to the channels associated with the beacon antenna. During the uplink transmission stage, the BS detects UEs' activity and data by using the proposed iterative detection algorithm, which consists of three modules: coarse data detection (DD), data-aided channel estimation (CE), and fine DD. In the proposed algorithm, the joint activity and DD is firstly performed based on the signals received by the beacon antenna. Subsequently, the DD is further refined by iteratively performing data-aided CE module and fine DD module using signals received by all BS antennas. Our simulation results demonstrate that the proposed scheme outperforms state-of-the-art mMIMO-based grant-free massive NOMA schemes with the same access latency. © 1967-2012 IEEE.

关键词： Channel estimation

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