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SSRN 2023年

作者： Zhao, Ruiyuan Li, Xiaotong Chen, Jian Wu, Pengxi College of Engineering China Agricultural University Beijing100083 China Key Laboratory of Smart Agricultural Technology in Tropical South China Ministry of Agriculture and Rural Affairs Guangzhou510642 China Key Laboratory of Spatial‐temporal Big Data Analysis and Application of Natural Resources in Megacities MNR Shanghai200063 China Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural Resources Shenzhen518000 China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China

In order to make the multi-rotor UAV more efficient to complete the contact operations such as picking, obstacle cleaning, grasping and inspired by the skeleton structure and motion law of the eagle's leg, this paper proposes a prototype design of a large load, wide range of movement, multiple DOF and collapsible manipulator. It is suitable for medium and larger size multi-rotor UAV. The main structure of the manipulator consists of a planetary reducer and a coupled parallel linkage mechanism. The reducer makes the manipulator more powerful, while the coupled parallel linkage mechanism can increase the movable range of the grasping mechanism. Based on the movement law of eagle’s claw, the grasping mechanism of the manipulator is designed. It can effectively increase the contact area between the target. In order to increase the DOF of the grasping mechanism, an ankle mechanism using a pair of double helical telescopic mechanisms to constrain the DOF of the universal joint is designed. It connects the grasping mechanism and the main structure of the manipulator. This paper shows the development process of the manipulator. The kinematic modeling and control of the manipulator are carried out. The ability of the manipulator system is tested, And the flight test experiment of the UAV carrying the manipulator is also executed. © 2023, The Authors. All rights reserved.

关键词： Manipulators

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USL-Net: Uncertainty Self-Learning Network for Unsupervised Skin Lesion Segmentation

arXiv

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arXiv 2023年

作者： Li, Xiaofan Peng, Bo Hu, Jie Ma, Changyou Yang, Daipeng Xie, Zhuyang School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu611756 China Data Recovery Key Laboratory of Sichuan Province Neijiang Normal University Sichuan Neijiang641100 China

Unsupervised skin lesion segmentation offers several benefits, such as conserving expert human resources, reducing discrepancies caused by subjective human labeling, and adapting to novel environments. However, segmenting dermoscopic images without manual labeling guidance is a challenging task due to image artifacts such as hair noise, blister noise, and subtle edge differences. In this paper, we introduce an innovative Uncertainty Self-Learning Network (USL-Net) to eliminate the need for manual labeling guidance for the segmentation. Initially, features are extracted using contrastive learning, followed by the generation of Class Activation Maps (CAMs) as saliency maps. High-saliency regions in the map serve as pseudo-labels for lesion regions while low-saliency regions represent the background. Besides, intermediate regions can be hard to classify, often due to their proximity to lesion edges or interference from hair or blisters. Rather than risking potential pseudo-labeling errors or learning confusion by forcefully classifying these regions, they are taken as uncertainty regions by exempted from pseudo-labeling and allowing the network to self-learning. Further, we employ connectivity detection and centrality detection to refine foreground pseudo-labels and reduce noise-induced errors. The performance is further enhanced by the iterated refinement process. The experimental validation on ISIC-2017, ISIC-2018, and PH2 datasets demonstrates that its performance is comparable to supervised methods, and exceeds that of other existing unsupervised methods. On the typical ISIC-2017 dataset, our method outperforms state-of-the-art unsupervised methods by 1.7% in accuracy, 6.6% in Dice coefficient, 4.0% in Jaccard index, and 10.6% in sensitivity. Copyright © 2023, The Authors. All rights reserved.

关键词： Contrastive Learning

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Enhancing Visible-Infrared Person Re-identification with Modality- and Instance-aware Visual Prompt Learning

arXiv

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arXiv 2024年

作者： Wu, Ruiqi Jiao, Bingliang Wang, Wenxuan Liu, Meng Wang, Peng School of Computer Science Northwestern Polytechnical University Shaanxi Xi’an China Ningbo Institute Northwestern Polytechnical University Zhejiang Ningbo China National Engineering Laboratory Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Xi’an China Honors College Northwestern Polytechnical University Shaanxi Xi’an China School of Electronics and Information Northwestern Polytechnical University Shaanxi Xi’an China

The Visible-Infrared Person Re-identification (VI ReID) aims to match visible and infrared images of the same pedestrians across non-overlapped camera views. These two input modalities contain both invariant information, such as shape, and modality-specific details, such as color. An ideal model should utilize valuable information from both modalities during training for enhanced representational capability. However, the gap caused by modality-specific information poses substantial challenges for the VI ReID model to handle distinct modality inputs simultaneously. To address this, we introduce the Modality-aware and Instance-aware Visual Prompts (MIP) network in our work, designed to effectively utilize both invariant and specific information for identification. Specifically, our MIP model is built on the transformer architecture. In this model, we have designed a series of modality-specific prompts, which could enable our model to adapt to and make use of the specific information inherent in different modality inputs, thereby reducing the interference caused by the modality gap and achieving better identification. Besides, we also employ each pedestrian feature to construct a group of instance-specific prompts. These customized prompts are responsible for guiding our model to adapt to each pedestrian instance dynamically, thereby capturing identity-level discriminative clues for identification. Through extensive experiments on SYSU-MM01 and RegDB datasets, the effectiveness of both our designed modules is evaluated. Additionally, our proposed MIP performs better than most state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Infrared imaging

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Improved Dota2 Lineup Recommendation Model Based on a Bidirectional LSTM

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Tsinghua Science and technology 2020年第6期25卷 712-720页

作者： Lei Zhang Chenbo Xu Yihua Gao Yi Han Xiaojiang Du Zhihong Tian Henan Key Laboratory of Big Data Analysis and Processing Kaifeng 457004China Institute of Data and Knowledge Engineering Henan UniversityKaifeng 475004.China National Internet Emergency Center Zhengzhou 450000China Department of Computer and Information Sciences Temple UniversityPhiladelphiaPA 19122-6008USA Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou 510006China

In recent years,e-sports has rapidly developed,and the industry has produced large amounts of data with specifications,and these data are easily to be *** to the above characteristics,data mining and deep learning methods can be used to guide players and develop appropriate strategies to win *** one of the world’s most famous e-sports events,Dota2 has a large audience base and a good game system.A victory in a game is often associated with a hero’s match,and players are often unable to pick the best lineup to *** solve this problem,in this paper,we present an improved bidirectional Long Short-Term Memory(LSTM)neural network model for Dota2 lineup *** model uses the Continuous Bag Of Words(CBOW)model in the Word2 vec model to generate hero *** CBOW model can predict the context of a word in a ***,a word is transformed into a hero,a sentence into a lineup,and a word vector into a hero vector,the model applied in this article recommends the last hero according to the first four heroes selected first,thereby solving a series of recommendation problems.

关键词： Word2vec mutiplayer online battle arena games Continuous Bag Of Words(CBOW)model Long Short-Term Memory(LSTM)

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Integrated Optimization of Maintenance Window and Train Timetable: An Innovation Modeling Approach and Alternating Direction Method of Multipliers Methods

SSRN

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SSRN 2022年

作者： Yang, Haonan Ni, Shaoqun Huo, Haoyang Yang, Yuhua Liu, Su Zhang, Qingpeng Chen, Dingjun School of Transportation and Logistics Southwest Jiaotong University Chengdu610031 China National and Local Joint Engineering Laboratory of Comprehensive Intelligent Transportation Southwest Jiaotong University Chengdu610031 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu610031 China School of Data Science City University of Hong Kong Hong Kong

Train timetable plays an important role in improving passenger service and transportation efficiency. The arrangement of the maintenance window directly affects the quality of maintenance, which interacts with the train timetable and constrains each other. This paper studies the integrated optimization of the maintenance window and train timetable. We abstract creatively the occupation of track capacity by maintenance activity as arc and construct the integrated time-space network of maintenance activities and trains. The headway constraints of different types of arcs are determined by the influences of trains and maintenances, and the original model is converted into a multi-commodity network flow model containing multiple types of arcs. Then we decomposed the problem into two sub-problems: (i) the arrangement of major maintenance activities;(ii) the integrated optimization of the maintenance window and train timetable for fixed maintenance activities. The heuristic algorithms based on VNS and ADMM framework were designed to solve the sub-problems, respectively. The practical case with the data of the Wuhan-Guangzhou Railway is analyzed. The results show that, in the scenario where the speed of the fast and slow trains is 160km/h and 120km/h, respectively, compared with common forms of the maintenance window, the optimized train operation cost is reduced by 21.7% with the completion of all maintenance activities. © 2022, The Authors. All rights reserved.

关键词： Heuristic algorithms

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Monthly streamflow forecasting with temporal-periodic transformer

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Journal of Hydrology 2025年 660卷

作者： Yin, Hanlin Zheng, Qirui Wei, Chenxu Liang, Congcong Fan, Minhao Zhang, Xiuwei Zhang, Yanning Shenzhen Research Institute of Northwestern Polytechnical University Sanhang Science & Technology Building No. 45th Gaoxin South 9th Road Nanshan District Shenzhen City518057 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University Xi'an710072 China School of Computer Science Northwestern Polytechnical University Xi'an710072 China Hydrology Bureau of the Yellow River Conservancy Commission Zhengzhou450004 China

Monthly streamflow forecasting is important for water resources planning and management in hydrology. In recent years, deep learning based data-driven approaches have received significant attention, especially the Long Short-Term Memory (LSTM) and the Transformer. Among the above two sorts of models for such a task, hardly any model considers the periodic information from the same month of different years directly. This periodic information is important for monthly streamflow forecasting and we propose a periodic attention mechanism to explore it in this paper. Specifically, we propose a novel Temporal-Periodic Transformer (TPT) model, which has temporal-periodic attention modules exploring the temporal information and the periodic information. As a comparison, the original Transformer-based streamflow forecasting model does not consider such periodic information explicitly. To show the performance of our TPT model, two datasets including the Catchment Attributes and Meteorology for Large-sample Studies in Australia (CAMELS-AUS) and a dataset from the Tangnaihai Hydrological Station located in Qinghai Province of China are employed in this paper. Our TPT model outperforms the benchmark Transformer model significantly, e.g., for Nash–Sutcliffe efficiency, the TPT model improves over the original Transformer-based model in 45.9% and furthermore its NSE achieves 0.9108 in Tangnaihai by pretraining in 20 selected basins in CAMELS-AUS. For monthly streamflow forecasting, the TPT model is a good choice. © 2025 Elsevier B.V.

关键词： Water management

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Tseng Splitting Method with Double Inertial Steps for Solving Monotone Inclusion Problems

arXiv

引用

arXiv 2022年

作者： Wang, Zhong-Bao Lei, Zhen-Yin Long, Xin Chen, Zhang-You Department of Mathematics Southwest Jiaotong University Chengdu Sichuan611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu Sichuan611756 China School of Mathematical Sciences University of Electronic Science and Technology of China Chengdu Sichuan611731 China

In this paper, based on double inertial extrapolation steps strategy and relaxed techniques, we introduce a new Tseng splitting method with double inertial extrapolation steps and self-adaptive step sizes for solving monotone inclusion problems in real Hilbert spaces. Under mild and standard assumptions, we establish successively the weak convergence, nonasymptotic O(√1n) convergence rate, strong convergence and linear convergence rate of the proposed algorithm. Finally, several numerical experiments are provided to illustrate the performance and theoretical outcomes of our algorithm. © 2022, CC BY-NC-SA.

关键词： Extrapolation

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Unsupervised video summarization with a convolutional attentive adversarial network

arXiv

引用

arXiv 2021年

作者： Liang, Guoqiang Lv, Yanbing Li, Shucheng Zhang, Shizhou Zhang, Yanning National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University Xi'an China

With the explosive growth of video data, video summarization, which attempts to seek the minimum subset of frames while still conveying the main story, has become one of the hottest topics. Nowadays, substantial achievements have been made by supervised learning techniques, especially after the emergence of deep learning. However, it is extremely expensive and difficult to collect human annotation for large-scale video datasets. To address this problem, we propose a convolutional attentive adversarial network (CAAN), whose key idea is to build a deep summarizer in an unsupervised way. Upon the generative adversarial network, our overall framework consists of a generator and a discriminator. The former predicts importance scores for all frames of a video while the latter tries to distinguish the score-weighted frame features from original frame features. Specifically, the generator employs a fully convolutional sequence network to extract global representation of a video, and an attention-based network to output normalized importance scores. To learn the parameters, our objective function is composed of three loss functions, which can guide the frame-level importance score prediction collaboratively. To validate this proposed method, we have conducted extensive experiments on two public benchmarks SumMe and TVSum. The results show the superiority of our proposed method against other state-of-the-art unsupervised approaches. Our method even outperforms some published supervised approaches. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Differentiable Feature Aggregation Search for Knowledge Distillation 16th

Differentiable Feature Aggregation Search for Knowledge Dist...

引用

16th European Conference on Computer Vision, ECCV 2020

作者： Guan, Yushuo Zhao, Pengyu Wang, Bingxuan Zhang, Yuanxing Yao, Cong Bian, Kaigui Tang, Jian Peking University Beijing China Technology Inc Beijing China National Engineering Laboratory for Big Data Analysis and Applications Beijing China DiDi AI Labs Beijing China

ISBN: (纸本)9783030585198

Knowledge distillation has become increasingly important in model compression. It boosts the performance of a miniaturized student network with the supervision of the output distribution and feature maps from a sophisticated teacher network. Some recent works introduce multi-teacher distillation to provide more supervision to the student network. However, the effectiveness of multi-teacher distillation methods are accompanied by costly computation resources. To tackle with both the efficiency and the effectiveness of knowledge distillation, we introduce the feature aggregation to imitate the multi-teacher distillation in the single-teacher distillation framework by extracting informative supervision from multiple teacher feature maps. Specifically, we introduce DFA, a two-stage Differentiable Feature Aggregation search method that motivated by DARTS in neural architecture search, to efficiently find the aggregations. In the first stage, DFA formulates the searching problem as a bi-level optimization and leverages a novel bridge loss, which consists of a student-to-teacher path and a teacher-to-student path, to find appropriate feature aggregations. The two paths act as two players against each other, trying to optimize the unified architecture parameters to the opposite directions while guaranteeing both expressivity and learnability of the feature aggregation simultaneously. In the second stage, DFA performs knowledge distillation with the derived feature aggregation. Experimental results show that DFA outperforms existing distillation methods on CIFAR-100 and CINIC-10 datasets under various teacher-student settings, verifying the effectiveness and robustness of the design. © 2020, Springer Nature Switzerland AG.

关键词： Distillation

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MIRROR: Multi-Modal Pathological Self-Supervised Representation Learning via Modality Alignment and Retention

arXiv

引用

arXiv 2025年

作者： Wang, Tianyi Fan, Jianan Zhang, Dingxin Liu, Dongnan Xia, Yong Huang, Heng Cai, Weidong School of Computer Science The University of Sydney SydneyNSW2006 Australia National Engineering Laboratory for Integrated AeroSpace-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China Research & Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen518057 China Ningbo Institute of Northwestern Polytechnical University Ningbo315048 China Department of Computer Science University of Maryland College ParkMD20742 United States

Histopathology and transcriptomics are fundamental modalities in cancer diagnostics, encapsulating the morphological and molecular characteristics of the disease. Multi-modal self-supervised learning has demonstrated remarkable potential in learning pathological representations by integrating diverse data sources. Conventional multi-modal integration methods primarily emphasize modality alignment, while paying insufficient attention to retaining the modality-specific intrinsic structures. However, unlike conventional scenarios where multi-modal inputs often share highly overlapping features, histopathology and transcriptomics exhibit pronounced heterogeneity, offering orthogonal yet complementary insights. Histopathology data provides morphological and spatial context, elucidating tissue architecture and cellular topology, whereas transcriptomics data delineates molecular signatures through quantifying gene expression patterns. This inherent disparity introduces a major challenge in aligning these modalities while maintaining modality-specific fidelity. To address these challenges, we present MIRROR, a novel multi-modal representation learning framework designed to foster both modality alignment and retention. MIRROR employs dedicated encoders to extract comprehensive feature representations for each modality, which is further complemented by a modality alignment module to achieve seamless integration between phenotype patterns and molecular profiles. Furthermore, a modality retention module safeguards unique attributes from each modality, while a style clustering module mitigates redundancy and enhances disease-relevant information by modeling and aligning consistent pathological signatures within a clustering space. Extensive evaluations on The Cancer Genome Atlas (TCGA) cohorts for cancer subtyping and survival analysis highlight MIRROR’s superior performance, demonstrating its effectiveness in constructing comprehensive oncological feature representations and be

关键词： Self-supervised learning

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