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International Conference on Guidance, Navigation and Control, ICGNC 2022

作者： Cao, Yuanyang Chen, Jian Xue, Zhentao Zhang, Zichao Hu, Gui College of Engineering China Agricultural University Beijing China Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities MNR Shanghai China Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural Resources Shenzhen China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijng China

ISBN: (纸本)9789811966125

Aiming at the problems that the classification of remote sensing image datasets is not balanced so that the accuracy of semantic segmentation is not high and the segmentation effect of scarce samples is poor. Transformer encoder with multihead self-attention mechanism is integrated into Deeplabv3+ network, and the attention mechanism is used to enhance the ability to capture global information, so as to improve the precision requirement of scarce categories in remote sensing semantic segmentation task. The algorithm firstly uniformly crops high-resolution remote sensing images into low-resolution image information for batch training and reducing information loss. Secondly, MobileNetV2, a lightweight backbone feature extraction network, is used to replace Xception network of Deeplabv3+. Finally, Transformer encoder based on multihead self-attention mechanism is connected in series with atrous spatial pyramid pooling (ASPP) of Deeplabv3+ encoder region to increase its segmentation precision by enhancing feature learning of scarce category samples in deep feature information. The experimental result shows that the proposed model called TransDeeplabv3+ gets 67.67% mIoU and 81.86% mPA on GID remote sensing dataset. Compared with Deeplabv3+ model, mIoU and mPA are improved by 9.46% and 8.52% respectively. TransDeeplabv3+ can effectively increase the precision because of the ability of increasing attention to the scarce species samples and solve the problem of the decrease of segmentation accuracy due to unbalanced data categories. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantic Segmentation

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Depositional process of hyperpycnal flow deposits:A case study on Lower Cretaceous Sangyuan outcrop in the Luanping Basin,Northeast China

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China Geology 2024年第3期7卷 505-516页

作者： De-zhi Yan Ru-kai Zhu Hao Shou Zhao-hui Xu Wei-hong Liu Si-cheng Zhu Zhi-cheng Lei Jing-ya Zhang Chang Liu Yi Cai Huai-min Xu Research Institute of Petroleum Exploration and Development PetroChinaBeijing 100083China National Energy Tight Oil and Gas R&D Center Beijing 100083China PetroChina Key Laboratory of Oil and Gas Reservoir Beijing 100083China State Key Laboratory of Petroleum Resources and Prospecting China University of Petroleum(Beijing)Beijing 102249China College of Geosciences China University of Petroleum(Beijing)Beijing 102249China Jiangxi Engineering Laboratory on Radioactive Geoscience and Big Data Technology Department of Information and EngineeringEast China University of TechnologyNanchang 330013China

Sedimentary process research is of great significance for understanding the distribution and characteristics of *** the detailed observation and measurement of the Sangyuan outcrop in Luanping Basin,this paper studies the depositional process of the hyperpycnal flow deposits,and divides their depositional process into three phases,namely,acceleration,erosion and *** the acceleration phase,hyperpycnal flow begins to enter the basin nearby,and then speeds up *** developed in the acceleration phase are *** addition,the original deposits become unstable and are taken away by hyperpycnal flows under the eroding *** a result,there are a lot of mixture of red mud pebbles outside the basin and gray mud pebbles within the *** the erosion phase,the reverse deposits are eroded and become thinner or even ***,no reverse grading characteristic is found in the proximal major channel that is closer to the source,but it is still preserved in the middle branch channel that is far from the *** entering the deceleration phase,normally grading deposits appear and cover previous *** final deposits in the basin are *** are reverse,and others are *** are superimposed with each other under the action of hyperpycnal *** analysis of the Sangyuan outcrop demonstrates the sedimentary process and distribution of hyperpycnites,and reasonably explain the sedimentary characteristics of *** is helpful to the prediction of oil and gas exploration targets in gravity flow deposits.

关键词： Hyperpycnal flow Sedimentary characteristics Depositional process Gravity flow deposit Hyperpycnite Red mud pebble Gray mud pebble Oil and gas exploration engineering Luanping Basin

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Impacts of aggregation method on the simulation of multispectral broadband reflectance 4

Impacts of aggregation method on the simulation of multispec...

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4th International Conference on Geology, Mapping, and Remote Sensing, ICGMRS 2023

作者： Chen, Feng Yu, Guanghao Gao, Zhibin Song, Yuejun College of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Big Data Institute of Digital Natural Disaster Monitoring in Fujian Xiamen University of Technology Xiamen361024 China Jiangxi Key Laboratory of Soil Erosion and Prevention Jiangxi Academy of Water Science and Engineering Nanchang330029 China

ISBN: (纸本)9781510672741

Simulation of multispectral broadband data from hyperspectral profiles is applicable for many purposes, mainly including sensor assessment and cross-calibration. For the simulation process, several aggregation methods have been proposed. In this paper, three aggregation methods were implemented, to investigate the impacts associated with method on the simulation of multispectral broadband. Comparative analyses were conducted in terms of channel reflectance and several derived vegetation indices, while mainly taking the wide field of view (WFV) sensor onboard Gaofen-6 for case study. Furthermore, impacts on the between-sensor comparability with the Landsat 8 Operational Land Imager (OLI) were discussed. Differences among aggregation methods in reflectance of the Gaofen-6 WFV varied along with channels, and obvious differences were observed in channels over near-infrared (770-890 nm) and red-edge (690-730 nm). Between-sensor difference relative to the Landsat 8 OLI was observed mainly over near-infrared, while the variations among aggregation methods were about 0.4%-2.0%. © 2024 COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Reflection

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KAN v.s. MLP for Offline Reinforcement Learning

KAN v.s. MLP for Offline Reinforcement Learning

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Guo, Haihong Li, Fengxin Li, Jiao Liu, Hongyan School of Information Renmin University of China China Institute of Medical Information Medical Library Chinese Academy of Medical Sciences Peking Union Medical College China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education China School of Economics and Management Tsinghua University China

ISBN: (纸本)9798350368741

Kolmogorov-Arnold Networks (KAN) is an emerging neural network architecture in machine learning. It has greatly interested the research community about whether KAN can be a promising alternative to the commonly used Multi-Layer Perceptions (MLP). Experiments in various fields demonstrated that KAN-based machine learning can achieve comparable if not better performance than MLP-based methods, but with much smaller parameter scales and are more explainable. In this paper, we explore the incorporation of KAN into the actor and critic networks for offline reinforcement learning (RL). We evaluated the performance, parameter scales, and training efficiency of various KAN and MLP-based conservative Q-learning (CQL) on the classical D4RL benchmark for offline RL. Our study demonstrates that KAN can achieve performance close to the commonly used MLP with significantly fewer parameters. This allows us to choose the base networks according to the offline RL task requirements. © 2025 IEEE.

关键词： KAN Kolmogorov-Arnold networks MLP multilayer perceptrons offline reinforcement learning

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Multi-UAV Logistics Planning Problem Based on Improved Genetic Simulated Annealing Algorithm

Multi-UAV Logistics Planning Problem Based on Improved Genet...

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International Conference on Guidance, Navigation and Control, ICGNC 2022

作者： Xue, Zhentao Chen, Jian Cao, Yuanyang Zhang, Zichao Liu, Xuzan College of Engineering China Agricultural University Beijing China Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities MNR Shanghai China Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural Resources Shenzhen China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijng China

ISBN: (纸本)9789811966125

Multi-drone logistics transportation and distribution have gradually become the focus of future logistics. So as to solve the multi-UAV logistics planning problem, the goal of this paper is to minimize the cost of take-off and variable-load flight, and builds a multi-UAV logistics planning model with constraints such as flight distance and logistics load. The Improved Genetic Simulated Annealing Algorithm(IGSAA) adds the SA mechanism on the basis of the classical GA, selectively adds non-optimal solutions and non-constrained solutions, and adopts the Improved Large Neighborhood Search Algorithm (ILNSA) to generate special algorithms with three strategies. By continuously updating the population iteration and selectively adding the probability according to the temperature change, the optimal UAV logistics planning is finally optimized. Finally, the simulation results close to reality show that compared with the initial random solution and the classical genetic algorithm, the improved genetic simulated annealing algorithm proposed in this paper reduces the flight cost of UAV by 16.78% and 8.49%, respectively, and can obtain lower transportation costs. Drone logistics solution. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Drones

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A knowledge-Enhanced Contrastive Learning Network for Weakly-Supervised Aspect Detection

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 506-517页

作者： Zhuoming Zheng Yi Cai Liuwu Li School of Software Engineering South China University of Technology Guangzhou China Key Laboratory of Big Data and Intelligent Robot Ministry of Education South China University of Technology Guangzhou China

Aspect detection, aiming at identifying the aspects of review segments, is a fundamental task in opinion mining and aspect-based sentiment analysis. Due to the high cost and time consuming of human-annotation for massive reviews, several unsupervised and weakly-supervised methods are proposed recently. However, existing weakly-supervised models are mostly seed-driven methods based on co-occurrence of words, which suffer from lacking the ability of detecting the aspects with infrequent aspect terms and identifying Misc aspect. To tackle these problems, we leverage external knowledge to enhance the representation of aspects and segments by a weakly-supervised method. In this paper, we propose an aspect knowledge-enhanced contrastive learning (AKECL) network with two powerful knowledge-enhanced encoders for aspects and reivew segments to enhance weakly-supervised aspect detection task. Experiments in seven different domains show that AKECL outperforms the competitive baselines, and demonstrate the effectiveness of our proposed method, as well as the improvement by introducing external knowledge.

关键词： Reviews Contrastive learning Vectors Feature extraction knowledge graphs Speech processing Sentiment analysis knowledge engineering Hardware Touch sensitive screens

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Biomedical knowledge graph construction of Sus scrofa and its application in anti-PRRSV traditional Chinese medicine discovery

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Animal Diseases 2025年第2期 220-234页

作者： Mingyang Cui Zhigang Hao Yanguang Liu Bomin Lv Hongyu Zhang Yuan Quan Li Qin Hubei Key Laboratory of Agricultural Bioinformatics College of InformaticsHuazhong Agricultural University Hubei Engineering Technology Research Center of Agricultural Big Data College of InformaticsHuazhong Agricultural University

As a new data management paradigm,knowledge graphs can integrate multiple data sources and achieve quick responses,reasoning and better predictions in drug *** by powerful contagion and a high rate of morbidity and mortality,porcine reproductive and respiratory syndrome(PRRS) is a common infectious disease in the global swine industry that causes economically great *** Chinese medicine(TCM) has advantages in low adverse effects and a relatively affordable cost of application,and TCM is therefore conceived as a possibility to treat PRRS under the current circumstance that there is a lack of safe and effective ***,we constructed a knowledge graph containing common biomedical data from humans and Sus Scrofa as well as information from thousands of ***,we validated the effectiveness of the Sus Scrofa knowledge graph by the t-SNE algorithm and selected the optimal model(i.e., transR) from six typical models,namely,transE,transR,DistMult,ComplEx,RESCAL and RotatE,according to five indicators,namely,MRR,MR,HITS@1,HITS@3 and HITS@*** on embedding vectors trained by the optimal model,anti-PRRSV TCMs were predicted by two paths,namely,VHC-Herb and VHPC-Herb,and potential anti-PRRSVTCMs were identified by retrieving the HERB database according to the phar-macological properties corresponding to symptoms of ***,Dan Shen's(Salvia miltiorrhiza Bunge)capacity to resist PRRSV infection was validated by a cell experiment in which the inhibition rate of PRRSV exceeded90% when the concentrations of Dan Shen extract were 0.004,0.008,0.016 and 0.032 mg/*** summary,this is the first report on the Sus Scrofa knowledge graph including TCM information,and our study reflects the important application values of deep learning on graphs in the swine industry as well as providing accessible TCM resources for PRRS.

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An Interactive Attention Mechanism Fusion Network for Aspect-Based Multimodal Sentiment Analysis

An Interactive Attention Mechanism Fusion Network for Aspect...

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2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Wang, Jinghong Gao, Yuan Li, Haokang College of Computer and Cyber Security Hebei Normal University Shijiazhuang050024 China Hebei Normal University Hebei Key Laboratory of Network and Information Security Shijiazhuang050024 China Hebei Normal University Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security Shijiazhuang050024 China Hebei Polytechnic Institute College of Artificial Intelligence and Big Data Shijiazhuang050000 China

ISBN: (纸本)9798350303780

The goal of aspect-based multimodal sentiment analysis (ABMSA) is to classify the sentiment associated with aspect words in a given context. Most current ABMSA models focus only on general inter-modal information interactions without considering both intra-modal and inter-modal information interactions and ignoring image noise. To address these issues, this paper proposes an Interactive Attention Mechanism Fusion Network (IAMFN) model. The model first designs an image-text fusion module based on the attention, which applies the attention mechanism to a recurrent neural network to fuse text and images while filtering the noise in the images, and finally adds the fused information to the aspect information step by step to obtain the dynamic inter-modal representation. In addition, this paper proposes an aspect-text fusion module based on the attention, which learns the intra-modal contextual representation by calculating the weights of each aspect word in the context. Finally, this paper stitches the information obtained from the two modules and feeds it into the fully connected softmax layers to predict sentiment polarity. We have conducted extensive experiments on two benchmark datasets and the experimental results show that our model achieves state-of-the-art performance. © 2023 IEEE.

关键词： Sentiment analysis

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Cutting Learned Index into Pieces: An In-depth Inquiry into Updatable Learned Indexes

Cutting Learned Index into Pieces: An In-depth Inquiry into ...

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International Conference on data engineering

作者： Jiake Ge Boyu Shi Yanfeng Chai Yuanhui Luo Yunda Guo Yinxuan He Yunpeng Chai Key Laboratory of Data Engineering and Knowledge Engineering MOE China School of Information Renmin University of China China

Numerous high-performance updatable learned indexes have recently been designed to support the writing requirements in practical systems. Researchers have proposed various strategies to improve the availability of updatable learned indexes. However, it is unclear which strategy is more profitable. Therefore, we deconstruct the design of learned indexes into multiple dimensions and in-depth evaluate their impacts on the overall performance, respectively. Through the in-depth exploration of learned indexes, we reckon that the approximation algorithm is the most crucial design dimension for improving the performance of the learned indexes rather than the popular works that focus on the learned index structure. Moreover, this paper makes a comprehensive end-to-end evaluation based on a high-performance key-value store to answer people’s concerns about which learned index is better and whether learned indexes can outperform traditional ones. Finally, according to end-to-end and in-depth evaluation results, we give some constructive suggestions on designing a better learned index in these dimensions, especially how to design an excellent approximate algorithm to improve the lookup and insertion performance of learned indexes.

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Frontiers of Artificial Intelligent and Quantitative Management: Preface for ITQM 2024

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Procedia Computer Science 2024年 242卷 1-8页

作者： Filip, Florin Gheorghe Shi, Yong Pocatilu, Paul Ciurea, Cristian-Eugen Li, Jianping Tien, James M. Berg, Daniel Romanian Academy Information Science and Technology Section Bucharest Romania Research Centre on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States Bucharest University of Economic Studies Bucharest Romania College of Engineering University of Miami Coral GablesFL33124 United States

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