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Hybrid Shunted Transformer embedding UNet for remote sensing image semantic segmentation

Neural Computing and Applications 2024年第25期36卷 15705-15720页

作者： Zhou, Huacong Xiao, Xiangling Li, Huihui Liu, Xiaoyong Liang, Peng School of Computer Science Guangdong Polytechnic Normal University Guangdong Guangzhou510630 China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangdong Polytechnic Normal University Guangdong Guangzhou510665 China School of Data Science and Engineering Guangdong Polytechnic Normal University Guangdong Guangzhou510630 China Academy of Heyuan Guangdong Polytechnic Normal University Guangdong Heyuan517099 China The Cyberspace Institute of Advanced Technology Guangzhou University Guangdong Guangzhou510006 China

With the development of deep learning, Remote Sensing Image (RSI) semantic segmentation has produced significant advances. However, due to the sparse distribution of the objects and the high similarity between classes, the task of semantic segmentation in RSI is still extremely challenging. In this paper, we propose a novel semantic segmentation framework for RSI called HST-UNet that can overcome the shortcomings of the existing models and extract and recover the global and local features of RSI, which is a hybrid semantic segmentation model with Shunted Transformer as encoder and Multi-Scale Convolutional Attention Network (MSCAN) as decoder. Then, to better fuse the information from the Encoder and the Decoder and alleviate the ambiguity, we design a Learnable Weighted Fusion (LWF) module to effectively connect to the decoder features. Extensive experiments demonstrate that the proposed HST-UNet outperforms the state-of-the-art methods, achieving F1 score/MIoU accuracy of 71.44%/83.00% on the ISPRS Vaihingen dataset and 77.36%/87.09% on ISPRS Potsdam dataset. The code will be available at https://***/HC-Zhou/HST-UNet. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Semantic Segmentation

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A coordinated bus bridging and metro short turning model in response to urban metro disruptions

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Transportation Safety and Environment 2022年第1期4卷 85-94页

作者： Zijian Wu Xiaolong Zhao Longwen Xiao Pu Wang Rail Data Research and Application Key Laboratory of Hunan Province School of Traffic and Transportation EngineeringCentral South UniversityChangsha 410075China

When an urban metro disruption occurs,the urban metro usually operates in a short turning mode and bus bridging is the typical way to relink the disconnected *** emergency response models dealing with urban metro disruptions,minimizing passenger delay in the bus bridging process and the metro short turning process is usually the optimization *** this study,we simultaneously consider the passenger delay in the bus bridging process and the metro short turning process to develop a coordinated emergency response model dealing with urban metro *** proposed coordinated model is validated in an actual urban metro line using actual passenger boarding demand *** insights in response to urban metro disruptions are obtained.

关键词： bus bridging metro short turning coordinated model urban metro disruption

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Application of an amphipathic molecule at the NiO_(x)/perovskite interface for improving the efficiency and long-term stability of the inverted perovskite solar cells

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Journal of Energy Chemistry 2023年第3期78卷 454-462,I0013页

作者： Guibin Shen Hongye Dong Fan Yang Xin Ren Ng Xin Li Fen Lin Cheng Mu Key Laboratory of Advanced Light Conversion Materials and Biophotonics Department of ChemistryRenmin University of ChinaBeijing 100872China Solar Energy Research Institute of Singapore(SERIS) National University of SingaporeSingapore 117574Singapore State Key Laboratory of Materials Processing and Die&Mold Technology School of Materials Science and EngineeringHuazhong University of Science and TechnologyWuhan 430074HubeiChina Department of Chemistry National University of Singapore3 Science Drive 3Singapore 117574Singapore

The presence of defects and detrimental reactions at NiO_(x)/perovskite interface extremely limit the efficiency performance and long-term stability of the perovskite solar cells(PSCs) based on NiO_(x).Herein,an amphipathic molecule Triton X100(Triton) is modified on the NiO_(x)*** hydrophilic chain of Triton as a Lewis base additive can coordinate with the Ni3+on the NiO_(x)surface which can passivate the interfacial defects and hinder the detrimental reactions at the NiO_(x)/perovskite ***,the hydrophobic chain of Triton protrudes from the NiO_(x)surface to prevent moisture from penetrating into the NiO_(x)/perovskite ***,the NiO_(x)/Triton-based devices(MAPbI3as absorbing layer) show superior moisture and thermal stability,retaining 88.4% and 64.3% of the initial power conversion efficiency after storage in air(40%-50% relative humidity(RH)) at 25 ℃ for 1070 h and in N2at 85℃ for 800 h,***,the efficiency increases from 17.59% to 19.89% because of the passivation defect and enhanced hole-extraction ***,the NiO_(x)/Triton-based PSCs with Cs_(0.05)(MA_(0.15)FA_(0.85))_(0.95)Pb(I_(0.85)Br_(0.15))3perovskite as the light-absorbing layer also exhibits better moisture and thermal stability compared to the control devices,indicating the viability of our *** particular note,a champion PCE of 22.35% and 20.46% was achieved for small-area(0.1 cm^(2)) and large-area(1.2 cm^(2)) NiO_(x)/Triton-based devices,respectively.

关键词： Perovskite solar cells NiO_(x) Defect passivation Long-term stability Amphipathic molecule

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A genetic programming approach with adaptive region detection to skin cancer image classification

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Journal of Automation and Intelligence 2024年第4期3卷 240-249页

作者： Kunjie Yu Jintao Lian Ying Bi Jing Liang Bing Xue Mengjie Zhang School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou450001China State Key Laboratory of Intelligent Agricultural Power Equipment Luoyang471000China Longmen Laboratory Luoyang471000China School of Electrical Engineering and Automation Henan Institute of TechnologyXinxiang453000China School of Engineering and Computer Science&Centre for Data Science and Artificial Intelligence Victoria University of WellingtonWellington6201New Zealand

Dermatologists typically require extensive experience to accurately classify skin *** recent years,the development of computer vision and machine learning has provided new methods for assisted *** skin cancer image classification methods have certain limitations,such as poor interpretability,the requirement of domain knowledge for feature extraction,and the neglect of lesion area information in skin *** paper proposes a new genetic programming(GP)approach to automatically learn global and/or local features from skin images for *** achieve this,a new function set and a new terminal set have been *** proposed GP method can automatically and flexibly extract effective local/global features from different types of input images,thus providing a comprehensive description of skin images.A new region detection function has been developed to select the lesion areas from skin images for feature *** performance of this approach is evaluated on three skin cancer image classification tasks,and compared with three GP methods and six non-GP *** experimental results show that the new approach achieves significantly better or similar performance in most *** analysis validates the effectiveness of our parameter settings,visualizes the multiple region detection functions used in the individual evolved by the proposed approach,and demonstrates its good convergence ability.

关键词： Genetic programming Skin cancer image classification Region detection Feature extraction

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Ensemble Classifier Design Based on Perturbation Binary Salp Swarm Algorithm for Classification

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Computer Modeling in engineering & Sciences 2023年第4期135卷 653-671页

作者： Xuhui Zhu Pingfan Xia Qizhi He Zhiwei Ni Liping Ni School of Management Hefei University of TechnologyHefei230009China School of Statistics Zhejiang Gongshang UniversityHangzhou310018China Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of EducationHefei230009China Collaborative Innovation Center for Statistical Data Engineering Technology and Application ZhejiangGongshang UniversityHangzhou310018China

Multiple classifier system exhibits strong classification capacity compared with single classifiers,but they require significant computational *** ensemble system aims to attain equivalent or better classification accuracy with fewer ***,current methods fail to identify precise solutions for constructing an ensemble *** this study,we propose an ensemble classifier design technique based on the perturbation binary salp swarm algorithm(ECDPB).Considering that extreme learning machines(ELMs)have rapid learning rates and good generalization ability,they can serve as the basic classifier for creating multiple candidates while using fewer computational ***,we introduce a combined diversity measure by taking the complementarity and accuracy of ELMs into account;it is used to identify the ELMs that have good diversity and low *** addition,we propose an ECDPB with powerful optimizing ability;it is employed to find the optimal subset of *** selected ELMs can then be used to forman ensemble *** on 10 benchmark datasets have been conducted,and the results demonstrate that the proposed ECDPB delivers superior classification capacity when compared with alternative methods.

关键词： Ensemble classifier salp swarmalgorithm diversity measure multiple classifiers system extreme learning machine

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laboratory simulation of CO_(2) immiscible gas flooding and characterization of seepage resistance

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Frontiers of Earth Science 2023年第3期17卷 797-817页

作者： Jie CHI Binshan JU Wenbin CHEN Mengfei ZHANG Rui ZHANG Anqi MIAO Dayan WANG Fengyun CUI School of Big Data and Fundamental Sciences Shandong Institute of Petroleum and Chemical TechnologyDongying 257061China School of Energy Resources China University of Geosciences(Beijing)Beijing 100083China Key Laboratory of Geological Evaluation and Development Engineering of Unconventional Natural Gas Energy Beijing 100083China School of Chemical Engineering Shandong Institute of Petroleum and Chemical TechnologyDongying 257061China

CO_(2) flooding can significantly improve the recovery rate, effectively recover crude oil, and has the advantages of energy saving and emission reduction. At present, most domestic researches on CO_(2) flooding seepage experiments are field tests in actual reservoirs or simulations with reservoir numerical simulators. Although targeted, the promotion is poor. For the characterization of seepage resistance, there are few studies on the variation law of seepage resistance caused by the combined action in the reservoir. To solve this problem, based on the mechanism of CO_(2), a physical simulation experiment device for CO_(2) non-miscible flooding production manner is designed. The device adopts two displacement schemes, gas-displacing water and gas-displacing oil, it mainly studies the immiscible gas flooding mechanism and oil displacement characteristics based on factors such as formation dip angle, gas injection position, and gas injection rate. It can provide a more accurate development simulation for the actual field application. By studying the variation law of crude oil viscosity and start-up pressure gradient, the characterization method of seepage resistance gradient affected by these two factors in the seepage process is proposed. The field test is carried out for the natural core of the S oilfield, and the seepage resistance is described more accurately. The results show that the advancing front of the gas drive is an arc, and the advancing speed of the gas drive oil front is slower than that of gas drive water;the greater the dip angle, the higher the displacement efficiency;the higher the gas injection rate is, the higher the early recovery rate is, and the lower the later recovery rate is;oil displacement efficiency is lower than water displacement efficiency;taking the actual core of S oilfield as an example, the mathematical representation method of core start-up pressure gradient in low permeability reservoir is established.

关键词： laboratory_simulation viscosity starting pressure gradient CO immiscible flooding characterization of seepage resistance

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Byzantine-Robust Federated Learning on Non-IID data via Inversing Artificial Gradients

Byzantine-Robust Federated Learning on Non-IID Data via Inve...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Li, Minyang Tang, Xiangyun Zhang, Tao Liu, Guangyuan Weng, Yu Minzu University of China Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance China Beijing Jiaotong University School of Software Engineering China Nanyang Technological University College of Computing and Data Science Singapore

ISBN: (纸本)9798350351255

Federated Learning (FL) is a distributed machine learning framework that enhances privacy by enabling multiple participants to train a global model without sharing their raw data. However, FL still faces the threat posed by data and Byzantine attacks (e.g., data poisoning and model poisoning attacks) from malicious clients aimed to decrease the FL global training accuracy. Previous studies have proposed solutions to improve the robustness of FL systems. However, these robust approaches have not effectively defended against Byzantine attacks from malicious clients in non-Independent and Identically Distributed (non-IID) environments, decreasing overall training accuracy. In this work to address this issues, we propose a new defence framework that aims to enhance model accuracy against Byzantine attacks from malicious clients in non-IID environments. In our approach, the central server performs the Inverse Deep Learning Gradient attack using the gradient data submitted by users in each round, obtaining an inversed artificial gradient. We then assess the squared L2 norm difference between this inversed gradient and the actual gradients to detect malicious clients. Simultaneously, we assign weights to each client involved in the aggregation based on differences in the squared L2 norm, aiming to minimize the impact of malicious actors on the overall model. We have experimentally evaluated our method and demonstrated its ability to maintain training accuracy under attack in non-IID environments using standard datasets. © 2024 IEEE.

关键词： Federated learning

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Cooperative Air-Ground Instant Delivery by UAVs and Crowdsourced Taxis 40

Cooperative Air-Ground Instant Delivery by UAVs and Crowdsou...

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40th IEEE International Conference on data engineering, ICDE 2024

作者： Gao, Junhui Wang, Qianru Zhang, Xin Shi, Juan Zhao, Xiang Han, Qingye Pan, Yan School of Computer Science Northwestern Polytechnical University China School of Computer Science and Technology Xidian University China Air Force Engineering University China National University of Defense Technology Laboratory for Big Data and Decision China School of Management Science and Real Estate Chongqing University China National University of Defense Technology National Key Laboratory of Information Systems Engineering China National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing China

ISBN: (纸本)9798350317152

Instant delivery has become a fundamental service in people's daily lives. Different from the traditional express service, the instant delivery has a strict shipping time constraint after being ordered. However, the labor shortage makes it challenging to realize efficient instant delivery. To tackle the problem, researchers have studied to introduce vehicles (i.e., taxis) or Unmanned Aerial Vehicles (UAVs or drones) into instant delivery tasks. Unfortunately, the delivery detour of taxis and the limited battery of UAVs make it hard to meet the rapidly increasing instant delivery demands. Under this circumstance, this paper proposes an air-ground cooperative instant delivery paradigm to maximize the delivery performance and meanwhile minimize the negative effects on the taxi passengers. Specifically, a data-driven delivery potential-demands-aware cooperative strategy is designed to improve the overall delivery performance of both UAVs and taxis as well as the taxi passengers' experience. The experimental results show that the proposed method improves the delivery number by 30.1% and 114.5% compared to the taxi-based and UAV-based instant delivery respectively, and shortens the delivery time by 35.7% compared to the taxi-based instant delivery. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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4D foetal cardiac ultrasound image detection based on deep learning with weakly supervised localisation for rapid diagnosis of evolving hypoplastic left heart syndrome

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1485-1499页

作者： Gang Wang Weisheng Li Mingliang Zhou Haobo Zhu Guang Yang Choon Hwai Yap Chongqing Key Laborotory of Image Rocognition Chongqing University of Posts and TelecommunicationsChongqingChina School of Computing and Data Engineering NingboTech UniversityNingboChina Department of Bioengineering Imperial College LondonLondonUK Key Laboratory of Cyberspace Big Data Intelligent Security(Chongqing University of Posts and Telecommunications) Ministry of EducationChongqingChina School of Computer Science Chongqing UniversityChongqingChina University of Oxford OxfordUK Cardiovascular Research Centre Royal Brompton HospitalLondonUK National Heart and Lung Institute Imperial College LondonLondonUK

Hypoplastic left heart syndrome(HLHS)is a rare,complex,and incredibly foetal congenital heart *** decrease neonatal mortality,evolving HLHS(eHLHS)in pregnant women should be critically diagnosed as soon as ***,diagnosis is currently heavily dependent on skilled medical professionals using foetal cardiac ultrasound images,making it difficult to rapidly and easily examine for this ***,the authors propose a cost-effective deep learning framework for rapid diagnosis of eHLHS(RDeH),which we have named ***,the framework implements a coarseto-fine two-stage detection approach,with a structure classification network for 4D human foetal cardiac ultrasound images from various spatial and temporal domains,and a fine detection module with weakly-supervised localisation for high-precision nidus localisation and physician *** experiments extensively compare the authors’network with other state-of-the-art methods on a 4D human foetal cardiac ultrasound image dataset and show two main benefits:(1)it achieved superior average accuracy of 99.37%on three categories of foetal ultrasound images from different cases;(2)it demonstrates visually fine detection performance with weakly supervised *** framework could be used to accelerate the diagnosis of eHLHS,and hence significantly lessen reliance on experienced medical physicians.

关键词： 4D deep learning fetal cardiac ultrasound hypoplastic left heart syndrome weakly-supervised learning

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A Dynamic Cross-Scale Transformer with Dual-Compound Representation for 3D Medical Image Segmentation 48

A Dynamic Cross-Scale Transformer with Dual-Compound Represe...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zhang, Ruixia Wang, Zhiqiong Wang, Zhongyang Xin, Junchang Northeastern University College of Medicine and Biological Information Engineering China Northeastern University School of Computer Science & Engineering China Northeastern University Key Laboratory of Big Data Management and Analytics Liaoning Province Shenyang China

ISBN: (纸本)9781728163277

Transformer models exploit multi-head self-attention to capture long-range information. Further, window-based self-attention solves the problem of quadratic computational complexity and provides a new solution for dense prediction of 3D images. However, Transformers miss structural information due to the naive tokenization scheme. Furthermore, single-scale attention fails to achieve a balance between feature representation and semantic information. Aiming at the above problems, we propose a window-based dynamic crossscale cross-attention transformer (DCS-Former) for precise representation of the diversity features. DCS-former first constructs dual-compound feature representations through Antehoc Structure-aware Module and Post-hoc Class-aware Module. Then, the bidirectional attention structure is designed to interactively fuse structural features with class representations. The experimental results show that our method outperforms various competing segmentation models on three different public datasets. © 2023 IEEE.

关键词： Deep Learning Medical Image Segmentation Self-attention Vision Transformer

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