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arXiv 2024年

作者： Li, Hongjuan Kang, Hui Sun, Geng Li, Jiahui Wang, Jiacheng Wang, Xue Niyato, Dusit Leung, Victor C.M. College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore College of Computing and Data Science Nanyang Technological University Singapore Department of Communication Engineering Jilin University Changchun130012 China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518115 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

Unmanned aerial vehicles (UAVs) have gained considerable attention as a platform for establishing aerial wireless networks and communications. However, the line-of-sight dominance in air-to-ground communications often leads to significant interference with terrestrial networks, reducing communication efficiency among terrestrial terminals. This paper explores a novel uplink interference mitigation approach based on the collaborative beamforming (CB) method in multi-UAV network systems. Specifically, the UAV swarm forms a UAV-enabled virtual antenna array (VAA) to achieve the transmissions of gathered data to multiple base stations (BSs) for data backup and distributed processing. However, there is a trade-off between the effectiveness of CB-based interference mitigation and the energy conservation of UAVs. Thus, by jointly optimizing the excitation current weights and hover position of UAVs as well as the sequence of data transmission to various BSs, we formulate an uplink interference mitigation multi-objective optimization problem (MOOP) to decrease interference affection, enhance transmission efficiency, and improve energy efficiency, simultaneously. In response to the computational demands of the formulated problem, we introduce an evolutionary computation method, namely chaotic non-dominated sorting genetic algorithm II (CNSGA-II) with multiple improved operators. The proposed CNSGA-II efficiently addresses the formulated MOOP, outperforming several other comparative algorithms, as evidenced by the outcomes of the simulations. Moreover, the proposed CB-based uplink interference mitigation approach can significantly reduce the interference caused by UAVs to non-receiving BSs. Copyright © 2024, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Msa-Net: A Medical Image Segmentation Network Based on Spatial Pyramid and Attention Mechanism

SSRN

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SSRN 2023年

作者： Feng, Yuncong Zhu, Xiaoyan Li, Yang Zhang, Xiaoli Lu, Huimin College of Computer Science and Engineering Changchun University of Technology Jilin Changchun130012 China Artificial Intelligence Research Institute Changchun University of Technology Jilin Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin Changchun130012 China Academy for Advanced Interdisciplinary Studies Northeast Normal University Jilin Changchun130024 China Jilin Provincial Smart Health Joint Innovation Laboratory for the New Generation of AI 3000 North Yuanda Avenue Gaoxin North District Changchun130102 China

Medical image segmentation is an important way to assist doctors to accurately diagnose diseases. However, semantic features are difficult to be fully extracted due to the complexity of medical image lesion tissue features (e.g., size, location, and morphology) and the high similarity between the background and the target region. To tackle these problems, we propose a novel medical image segmentation network MSA-Net based on the spatial pyramid and attention mechanism. This technique facilitates the extraction of multi-scale spatial information and the establishment of channel dependencies within images. By incorporating efficient pyramid attention and channel-spatial dual attention modules, the network autonomously focuses on lesion regions while suppressing background interference. Additionally, the SPCA (Spatial Pyramid-Coordinate Attention) module is integrated at the bottleneck layer, enabling the assimilation of larger receptive field information and prioritizing relevant features. This augmentation improves the network's ability to gather both location and channel-based insights. We proceed to evaluate our model qualitatively and quantitatively across four public datasets: ISIC2018, Lung segmentation, Kvasir-SEG, and ISLES2022. The results substantiate the superior performance of our proposed model. © 2023, The Authors. All rights reserved.

关键词： Deep learning

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A Survey on Fairness in Large Language Models

arXiv

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arXiv 2023年

作者： Li, Yingji Du, Mengnan Song, Rui Wang, Xin Wang, Ying College of Computer Science and Technology Jilin University Changchun130012 China Department of Data Science New Jersey Institute of Technology Newark United States School of Artificial Intelligence Jilin University Changchun130012 China Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun130012 China

Large Language Models (LLMs) have shown powerful performance and development prospects and are widely deployed in the real world. However, LLMs can capture social biases from unprocessed training data and propagate the biases to downstream tasks. Unfair LLM systems have undesirable social impacts and potential harms. In this paper, we provide a comprehensive review of related research on fairness in LLMs. Considering the influence of parameter magnitude and training paradigm on research strategy, we divide existing fairness research into oriented to medium-sized LLMs under pre-training and fine-tuning paradigms and oriented to large-sized LLMs under prompting paradigms. First, for medium-sized LLMs, we introduce evaluation metrics and debiasing methods from the perspectives of intrinsic bias and extrinsic bias, respectively. Then, for large-sized LLMs, we introduce recent fairness research, including fairness evaluation, reasons for bias, and debiasing methods. Finally, we discuss and provide insight on the challenges and future directions for the development of fairness in LLMs. Copyright © 2023, The Authors. All rights reserved.

关键词： Computational linguistics

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Phenotyping of Salvia miltiorrhiza Roots Reveals Associations between Root Traits and Bioactive Components

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Plant Phenomics 2023年第4期5卷 746-756页

作者： Junfeng Chen Yun Wang Peng Di Yulong Wu Shi Qiu Zongyou Lv Yuqi Qiao Yajing Li Jingfu Tan Weixu Chen Ma Yu Ping Wei Ying Xiao Wansheng Chen The SATCM Key Laboratory for New Resources&Quality Evaluation of Chinese Medicine Institute of Chinese Materia MedicaShanghai University of Traditional Chinese MedicineShanghai 201203China School of Medicine Shanghai UniversityShanghai 200444China State Local Joint Engineering Research Center of Ginseng Breeding and Application Jilin Agricultural UniversityChangchun 130118China School of Computer Science Sichuan Normal UniversityChengdu 610066China Shangyao Huayu(Linyi)Traditional Chinese Resources Co. Ltd.Linyi 276000China School of Life Science and Engineering Southwest University of Science and TechnologyMianyang 621010SichuanChina Sichuan Academy of Traditional Chinese Medicine Chengdu 610041China Department of Pharmacy Changzheng HospitalSecond Military Medical UniversityShanghai 200003China

Plant phenomics aims to perform high-throughput,rapid,and accurate measurement of plant traits,facilitating the identification of desirable traits and optimal genotypes for crop *** miltiorrhiza(Danshen)roots possess remarkable therapeutic effect on cardiovascular diseases,with huge market *** great advances have been made in metabolic studies of the bioactive metabolites,investigation for *** roots on other physiological aspects is ***,we developed a framework that utilizes image feature extraction software for in-depth phenotyping of *** *** employing multiple software programs,*** roots were described from 3 aspects:agronomic traits,anatomy traits,and root system *** K-means clustering based on the diameter ranges of each root branch,all roots were categorized into 3 groups,with primary root-associated key *** a proof of concept,we examined the phenotypic components in a series of randomly collected *** roots,demonstrating that the total surface of root was the best parameter for the biomass prediction with high linear regression correlation(R^(2)=0.8312),which was sufficient for subsequently estimating the production of bioactive metabolites without content *** study provides an important approach for further grading of medicinal materials and breeding practices.

关键词： Root utilize anatomy

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Static Gesture Recognition using CNN with Hand Landmark Detection 2

Static Gesture Recognition using CNN with Hand Landmark Dete...

引用

2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing, AIAHPC 2022

作者： Lyu, Leyu Wang, Dixuan Zhang, Sibo Zhao, Yanran Zhou, Sicheng College of Information Engineering China Jiliang University Hangzhou China Bradly Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University Blacksburg United States College of Automotive Engineering Jilin University Changchun China College of Mechanical Engineering Donghua University Shanghai China College of Letters and Science University of California Los Angeles Los Angeles United States

ISBN: (纸本)9781510657717

Static Gesture Recognition is an interactive system that enables people, especially the hearing-impaired, to directly communicate with other people and machines without third-party auxiliary equipment. However, it is challenging due to gesture datasets and the recognition accuracy of pre-processing. This paper proposes Convolutional Neural Network (CNN) with hand landmark detection to recognize Static Gestures and improve the pre-processing accuracy significantly. First, this paper makes a dataset by taking 5,800 photos from 4 people, including 13 one-handed gestures and 3 two-handed. Then, it pre-processes the dataset in both ways of skin color detection and hand landmark detection to select a highaccuracy method. Finally, CNN is trained for both pre-processed datasets to classify the samples into 16 classes. To verify the method's effectiveness, this paper also compares a baseline model that comprises the Support Vector Machine and the Principle Component Analysis. The experimental result shows that CNN eclipses the other model with higher accuracy of 99% for Static Gesture Recognition. © 2022 SPIE. All rights reserved.

关键词： Support vector machines

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Localization of Dummy Data Injection Attacks in Power Systems Considering Incomplete Topological Information: A Spatio-Temporal Graph Wavelet Convolutional Neural Network Approach

arXiv

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arXiv 2024年

作者： Qu, Zhaoyang Dong, Yunchang Li, Yang Song, Siqi Jiang, Tao Li, Min Wang, Qiming Wang, Lei Bo, Xiaoyong Zang, Jiye Xu, Qi School of Electrical Engineering Northeast Electric Power University Jilin China Jilin Electric Power Research Institute of State Grid Changchun China Northeast Electric Power Design and Institute Co. Ltd. China Power Engineering Consulting Group China School of Computer Northeast Electric Power University Jilin China Jilin Engineering Technology Research Center of Intelligent Electric Power Big Data Processing Jilin China Department of Civil and Environmental Engineering The Hong Kong University of Science and Technology Hong Kong

The emergence of novel the dummy data injection attack (DDIA) poses a severe threat to the secure and stable operation of power systems. These attacks are particularly perilous due to the minimal Euclidean spatial separation between the injected malicious data and legitimate data, rendering their precise detection challenging using conventional distance-based methods. Furthermore, existing research predominantly focuses on various machine learning techniques, often analyzing the temporal data sequences post-attack or relying solely on Euclidean spatial characteristics. Unfortunately, this approach tends to overlook the inherent topological correlations within the non-Euclidean spatial attributes of power grid data, consequently leading to diminished accuracy in attack localization. To address this issue, this study takes a comprehensive approach. Initially, it examines the underlying principles of these new DDIAs on power systems. Here, an intricate mathematical model of the DDIA is designed, accounting for incomplete topological knowledge and alternating current (AC) state estimation from an attacker's perspective. This model aims to mitigate the vulnerabilities associated with direct current (DC) flow-based attack data generation methods that can be readily detected. Subsequently, by integrating a priori knowledge of grid topology and considering the temporal correlations within measurement data and the topology-dependent attributes of the power grid, this study introduces temporal and spatial attention matrices. These matrices adaptively capture the spatio-temporal correlations within the attacks. Leveraging gated stacked causal convolution and graph wavelet sparse convolution, the study jointly extracts spatio-temporal DDIA features. This methodology significantly enhances the dynamic correlation mining capability while improving computational efficiency in the neighborhood. Finally, the research proposes a DDIA localization method based on spatio-temporal graph

关键词： Convolution

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End-to-end efficient cascade license plate recognition system in unconstrained scenarios

End-to-end efficient cascade license plate recognition syste...

引用

Power Electronics, computer Applications (ICPECA), IEEE International Conference on

作者： Weiliang Peng Zhenpeng Hu Tianyu Liang Institute of Microscale Optoelectronics Shenzhen University Shenzhen China School of Mechanichal Engineering Nanjing University of Science and Technology Nanjing China College of Computer Science and Technology Jilin University Changchun China

Automatic License Plate Recognition (ALPR) has become a necessity for today’s urban information and transportation systems. However, the variable and unconstrained environment in real-world applications makes the license plate (LP) detection and recognition task still challenging. In this work, we design a robust end-to-end trainable LP recognition system. It employs a simple cascade framework to achieve efficient and accurate LP detection and recognition tasks. The system corrects LPs in the LP detection phase, which greatly improves LP recognition accuracy in nonideal views. Adding the attention module (containing both channel attention and spatial attention) to the LP recognition network enhances the adaptability of the network and helps the accurate delineation of characters. Experimental results show an average recognition accuracy of 98.7% on the CCPD dataset and a very good 94.4% on the Challenge sub-dataset. In addition, simultaneous recognition of multiple LPs can be achieved on the invisible CRPD dataset, demonstrating the excellent generalization capability of our system.

关键词： Training Transportation computer applications Power electronics Classification algorithms Character recognition Task analysis

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Joint-Motion Mutual Learning for Pose Estimation in Videos

arXiv

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arXiv 2024年

作者： Wu, Sifan Chen, Haipeng Yin, Yifang Hu, Sihao Feng, Runyang Jiao, Yingying Yang, Ziqi Liu, Zhenguang College of Computer Science and Technology Jilin University Changchun China A*STAR Singapore Georgia Institute of Technology Atlanta United States School of Artificial Intelligence Jilin University Changchun China The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun China Institute of Blockchain and Data Security Hangzhou China

Human pose estimation in videos has long been a compelling yet challenging task within the realm of computer vision. Nevertheless, this task remains difficult because of the complex video scenes, such as video defocus and self-occlusion. Recent methods strive to integrate multi-frame visual features generated by a backbone network for pose estimation. However, they often ignore the useful joint information encoded in the initial heatmap, which is a byproduct of the backbone generation. Comparatively, methods that attempt to refine the initial heatmap fail to consider any spatiotemporal motion features. As a result, the performance of existing methods for pose estimation falls short due to the lack of ability to leverage both local joint (heatmap) information and global motion (feature) dynamics. To address this problem, we propose a novel joint-motion mutual learning framework for pose estimation, which effectively concentrates on both local joint dependency and global pixel-level motion dynamics. Specifically, we introduce a context-aware joint learner that adaptively leverages initial heatmaps and motion flow to retrieve robust local joint feature. Given that local joint feature and global motion flow are complementary, we further propose a progressive joint-motion mutual learning that synergistically exchanges information and interactively learns between joint feature and motion flow to improve the capability of the model. More importantly, to capture more diverse joint and motion cues, we theoretically analyze and propose an information orthogonality objective to avoid learning redundant information from multi-cues. Empirical experiments show our method outperforms prior arts on three challenging benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Motion estimation

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Study on the Extraction of Target Contours of Underwater Images 6th

Study on the Extraction of Target Contours of Underwater Ima...

引用

6th International Conference on Artificial Intelligence and Security, ICAIS 2020

作者： Tong, Luyan Wei, Fenglin Pan, Yupeng Wang, Kai Changchun Architecture and Civil Engineering College Changchun China College of Computer Science and Technology Jilin University Changchun China

ISBN: (纸本)9783030578831

Image is an important medium for human to obtain information, and the study of underwater image information plays an important role in marine scientific research. This paper studies the target contour extraction in the complex underwater environment. In this paper, we focus on the external contour calibration, and the study aims to segment the contours of many images with prominent texture, chromatic aberration, and shape differences. We also improve underwater image denoise and enhancement technology, restore target area features as much as possible, and process color channel selection with K-means algorithm. At the same time, the external contour extraction and the filling of the target area are partially optimized. Multi-scale watershed algorithm is used to improve the performance of the algorithm. Finally, the Canny and Grabcut algorithms are selected to detect the edges of the image. The experimental results verify that our method is feasible to extract the contours of fish in complex environments. © 2020, Springer Nature Switzerland AG.

关键词： K-means clustering

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A Novel Adaptive Unscented Kalman Filter Algorithm for SOC Estimation to Reduce the Sensitivity of Attenuation Coefficient

SSRN

引用

SSRN 2024年

作者： Zhou, Zhenhu Zhan, Mingjing Wu, Baigong Xu, Guoqi Zhang, Xiao Cheng, Chen School of Naval Architecture and Ocean Engineering Jiangsu University of Science and Technology Jiangsu Province Zhenjiang212003 China Marine Equipment and Technology Institute Jiangsu University of Science and Technology Jiangsu Province Zhenjiang212003 China College of Automation Jiangsu University of Science and Technology Jiangsu Province Zhenjiang212003 China School of Energy and Power Engineering Changchun Institute of Technology Jilin Province Changchun130012 China

An exceptional exponential adaptive The Strong Tracking Unscented Kalman filter technique is designed to overcome the issues of sluggish tracking speed, delayed response to changes in model parameters, and inaccurate prediction of battery state of charge (SOC) at different temperatures. This strategy decreases the sensitivity of the attenuation coefficient. The method determines the attenuation factor by establishing a correlation between current and previous residuals. An exponential model continuously adjusts the Kalman gain and contribution rate, hence improving the accuracy of the current data. The noise covariance matrix is stabilized by controlling the attenuation factor to remain within a predetermined range. The contribution rate is utilized to estimate the statistical characteristics of detection noise, however it may exhibit bias. A comparison was made between the computed and experimental pulse discharge data. The astrukf method exhibits a maximum estimation error of 10-8 at 0 ° C and 10-12 at 15 and 25 ° C. This value is well below the levels of the UKF and EKF algorithms. The astrukf algorithm outperforms the UKF and EKF algorithms in terms of prediction accuracy at different temperatures. The astrukf method is well-suited for estimating SOC and has good adaptability to temperature variations. © 2024, The Authors. All rights reserved.

关键词： Covariance matrix

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