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An end-to-end repair-based joint training framework for weakly supervised pavement crack segmentation

Multimedia Tools and applications 2024年 1-19页

作者： Zhou, Hui Tao, Huanjie Duan, Qianyue Hu, Zhenwu Deng, Yishi School of Computer Science Northwestern Polytechnical University Xi’an710129 China School of Cyberspace Security Northwestern Polytechnical University Xi’an710129 China Engineering Research Center of Embedded System Integration Ministry of Education Xi’an710129 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi’an710129 China

Weakly supervised pavement crack segmentation aims to assign each pixel of pavement surface images a category label (crack or non-crack) using limited annotation information. Most existing methods adopt the multi-stage approach based on the class activation map (CAM), which outputs from a pre-trained crack classification model, to generate the pixel-level pseudo labels, and then train a segmentation model using these pseudo labels. However, these methods have two notable drawbacks: (i) CAM focuses primarily on the most discriminative regions of the image, potentially resulting in an incomplete estimation of crack regions, and (ii) the process of obtaining pixel-level pseudo labels from CAM is complex and may introduce noise. To avoid these issues, we propose an end-to-end repair-based joint training framework for weakly supervised pavement crack segmentation. Specifically, to better guide the model to pay more attention to the repair process of crack regions, we introduce the segmentation loss, which assigns different weights for crack regions and background regions, through the Residual Project Network. Additionally, to alleviate the impact of the domain gap between synthetic and real datasets, we introduce the consistency loss through the joint training paradigm of unpaired data that incorporates both crack and non-crack images into the training process simultaneously. Furthermore, to enable the model to effectively extract local features and reduce the model's computational complexity, we introduce the Lightweight Multi-head Self-attention module. The experiment results on the CRACK500 dataset, the CFD dataset, and the AEL dataset demonstrate that our model achieves better performance than some existing weakly supervised pavement segmentation methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Repair

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Time series forecast of sales volume based on XGBoost 2

Time series forecast of sales volume based on XGBoost

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2021 2nd International Workshop on Electronic communication and Artificial Intelligence, IWECAI 2021

作者： Zhang, Lingyu Bian, Wenjie Qu, Wenyi Tuo, Liheng Wang, Yunhai School of Computer Science and Technology Shandong University Qingdao China Didi AI Labs Didi Chuxing Beijing China Smart Transportation Center National Engineering Laboratory for Big Data Analysis and Applications Beijing China

Some problems such as the decline of new labor force, the increase of retired labor force emerge because of the complex and changeable market environment, consequently exacerbating the staffing problem in the retail industry. Also, the unreasonable distribution of personnel, there are few people in busy hours and too many people in idle hours, which causes waste of labor. To address this issue, we analyzed the time series of sales volume in the retail industry in detail, and processed the data with feature engineering for predicting the in-store sales volume in the future. At the same time, other features such as weather and temperature are added to improve the accuracy of the model. Considering the characteristics of the data, we choose XGBoost as the prediction model. The experiments on real-world datasets verified better performance of proposed model compared with other state-of-art models. © Published under licence by IOP Publishing Ltd.

关键词： Time series

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Example-based web page recoloring method

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Frontiers of Computer Science 2020年第5期14卷 229-231页

作者： Yonghao Long Zhihao Zang Xiangping Chen Fan Zhou Xiaonan Luo School of Data and Computer Science National Engineering Research Center of Digital LifeSun Yat-sen UniversityGuangzhou510006China Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion The School of Communication and DesignSun Yat-sen UniversityGuangzhou510006China School of Computer Science and Information Security Guilin University of Electronic TechnologyGuilin541004China

1 Introduction The user interface(UI)is very important for a software product,and a well-designed UI will attract users and make a good *** has been shown that color plays an important role in invoking emotional reactions[1],influencing usability[2],forming first impressions[3],and affecting the energy cost of the UI[4].Generating satisfying color schemes has high requirements for experience,aesthetic,and creative inspiration of designers[5].

关键词： web coloring satisfying

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Leveraging Interactive Paths for Sequential Recommendation 28th

Leveraging Interactive Paths for Sequential Recommendation

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28th International Conference on database Systems for Advanced applications, DASFAA 2023

作者： Li, Aoran Zang, Yalei Wang, Yani Li, Bohan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China Key Laboratory of Safety-Critical Software Ministry of Industry and Information Technology Beijing China National Engineering Laboratory for Integrated Aero-Space-Ground Ocean Big Data Application Technology Xi’an China

ISBN: (纸本)9783031306716

Sequential recommendation systems dynamically predict the users’ next behaviors from chronological historical records to provide more accurate recommendations. However, in many real-world scenarios, recommenders find it difficult to make recommendations for cold-start users who have limited interactions with items. To address this issue, existing methods utilize auxiliary information to capture user preferences, which have achieved performance improvement to some extent. But with the gradual awareness of personal privacy, external information becomes difficult to obtain. Therefore, we develop a novel model called Path-based sequential Recommender for cold-start users (PathRec). PathRec exploits the rich sequential dependencies within the interactive paths to give reasonable recommendations for cold-start users without additional external information. Specifically, (i) PathRec constructs the global graph to learn the representations of the items. (ii) Then, PathRec elaborately selects the sampled paths to derive the underlying dependencies of the cold-start sequence. (iii) Finally, PathRec provides rich and complementary information to user-item interactions by strongly coupling the paths to give suitable recommendations. Extensive experiments on three datasets demonstrate the effectiveness of PathRec. In particular, our model can adapt to the cold-start users and has potential interpretability for recommendations. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Recommender systems

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Research on trans-region integrated traffic emergency dispatching technology based on multi-agent

Research on trans-region integrated traffic emergency dispat...

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作者： Yang, Tingyu Peng, Xiaoqian Chen, Dingjun Yang, Feiyu Muneeb Abid, Malik School of Transportation and Logistics Southwest Jiaotong University Chengdu China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest JiaoTong University Chengdu China National and Local Joint Engineering Laboratory of Comprehensive Intelligent Transportation Southwest Jiaotong University Chengdu China Department of Civil Engineering College of Engineering and Technology University of Sargodha Sargodha Pakistan

With the increasingly close links between provinces and municipalities, the complexity and social connectivity of emergencies have gradually increased. How to realize the rapid and scientific dispatch of emergency resources based on the comprehensive transportation network has become a realistic need. However, due to barriers in management and communication between different administrative regions and different modes of transportation, emergency resource scheduling can only be carried out in the region, and the utilization of integrated traffic channels is insufficient to form a good multimodal transport system. The multi-agent system has strong self-organization ability, learning ability and reasoning ability, which solves problems in the fields of dynamic decision-making and micro-simulation and provides ideas for solving the problem of comprehensive traffic emergency dispatching across regions. In this paper, the multi-agent system is introduced into the research of the task assignment problem of trans-regional comprehensive traffic emergency materials dispatching, based on the traditional bidding rules, the emergency dispatch task assignment rules are proposed and multi-agent trans-regional comprehensive traffic emergency dispatching model based on improved bidding rules is established. The simulation results show that the proposed method can break the regional barrier and form a multimodal transport scheme for emergency materials from the reserve point to the demand point under the minimum generalized time cost, providing decision support for emergency dispatch. © 2020 - IOS Press and the authors. All rights reserved.

关键词： Multi agent systems

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A New Free-Standing Aqueous Zinc-Ion Capacitor Based on MnO2–CNTs Cathode and MXene Anode

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Nano-Micro Letters 2019年第4期11卷 227-238页

作者： Siliang Wang Qiang Wang Wei Zeng Min Wang Limin Ruan Yanan Ma Key Laboratory of Intelligent Computing and Signal Processing Ministry of EducationAnhui UniversityNo.3 Feixi RoadHefei 230039Anhui ProvincePeople’s Republic of China National Engineering Research Center for Agro-Ecological Big Data Analysis and Application School of Electronics and Information EngineeringAnhui UniversityNo.111 Jiulong RoadHefei 230601Anhui ProvincePeople’s Republic of China School of Sciences Hubei University of Automotive TechnologyNo.167 Checheng West RoadShiyan 442002Hubei ProvincePeople’s Republic of China

Restricted by their energy storage mechanism,current energy storage devices have certain drawbacks,such as low power density for batteries and low energy density for ***,the nearest ion capacitors,such as lithium-ion and sodium-ion capacitors containing battery-type and capacitor-type electrodes,may allow achieving both high energy and power *** the inspiration,a new zinc-ion capacitor(ZIC)has been designed and realized by assembling the free-standing manganese dioxide-carbon nanotubes(MnO2-CNTs)battery-type cathode and MXene(Ti3C2Tx)capacitortype anode in an aqueous *** ZIC can avoid the insecurity issues that frequently occurred in lithium-ion and sodium-ion capacitors in organic *** expected,the ZIC in an aqueous liquid electrolyte exhibits excellent electrochemical performance(based on the total weight of cathode and anode),such as a high specific capacitance of 115.1 F g?1(1 mV s?1),high energy density of 98.6 Wh kg?1(77.5 W kg?1),high power density of 2480.6 W kg?1(29.7 Wh kg?1),and high capacitance retention of^83.6%of its initial capacitance(15,000 cycles).Even in an aqueous gel electrolyte,the ZIC also exhibits excellent *** work provides an essential strategy for designing next-generation high-performance energy storage devices.

关键词： Energy storage Zinc-ion capacitor Battery-type and capacitor-type electrodes MXene Electrochemical performance

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Research on Equipment Operation and Maintenance Management technology of Large Railway Passenger Station 5th

Research on Equipment Operation and Maintenance Management T...

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5th International Conference on Smart Vehicular technology, Transportation, Communication and applications, VTCA 2022

作者： Wang, Bozhou Li, Lexi Ni, Shaoquan Chen, Dingjun School of Transportation and Logistics Southwest Jiaotong University Sichuan Chengdu610031 China National and Local Joint Engineering Laboratory of Comprehensive Intelligent Transportation Southwest Jiaotong University Sichuan Chengdu610031 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Sichuan Chengdu610031 China

ISBN: (纸本)9789819908479

With the development of informationization and intelligence of railway passenger stations, problems such as inconvenient information interaction, missing operation and maintenance data, and difficulty in accurate positioning of equipment under the existing equipment operation and maintenance management mode have become the focus. In this paper, the operation and maintenance management process of equipment is divided into three stages: fault prediction and warning, fault diagnosis and processing, and fault rule summary. The implementation schemes of key technologies such as data warehouse, data mining, 5G fusion positioning, and electronic fence are given to realize functions such as condition assessment, fault prediction, fault diagnosis, precise positioning, fence warning, and auxiliary decision-making, which can meet the needs of managers and operations people. Research will help to improve the efficiency and safety of equipment operation and maintenance, and have a reference significance for integrated intelligent operation and maintenance technology and the construction of modern passenger stations. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： data mining

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Secondary Access Class Barring for Machine Type Communications with PUSCH Resource Constraints 100

Secondary Access Class Barring for Machine Type Communicatio...

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100th IEEE Vehicular technology Conference, VTC 2024-Fall

作者： Wen, Yuxuan Yang, Liu Bai, Chaoyuan Liu, Heng Chengdu611756 China Southwest Jiaotong University Key Lab of Information Coding & Transmission Institude of Mobile Communication Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu611756 China

ISBN: (纸本)9798331517786

With the continuous increase of IoT devices, multiple devices desire to use network resources simultaneously during peak hours and overload conditions, leading to collisions and a reduction in the network's effective throughput. The Access Class Barring (ACB) method has been proposed as the optimal potential solution in LTE communication networks to overcome this problem. However, existing ACB schemes do not fully utilize preamble resources, and most schemes do not consider the situation where PUSCH resources are limited. Therefore, this paper proposes a two-stage ACB algorithm that considers the situation of limited PUSCH resources. This algorithm recycles part of the preamble resources and sets the ACB factor based on the number of PUSCH resources. Simulation results show that our proposed algorithm significantly outperforms traditional schemes in terms of total service time, delay, throughput, and PUSCH resource efficiency. © 2024 IEEE.

关键词： Internet of things

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Cooperative Ramp Merging for Mixed Traffic with Connected Automated Vehicles and Human-Operated Vehicles

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IFAC-PapersOnLine 2019年第24期52卷 76-81页

作者： Tianyu Huang Zhanbo Sun School of Transportation and Logistics Southwest Jiaotong University China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu China

The rapid theoretical development and commercialization of connected automated vehicle (CAV) has led to the problem of mixed traffic, i.e., traffic mixed with CAVs and conventional human-operated vehicles (HVs). The paper studies cooperative ramp merging for mixed traffic. Using discrete optimization, a cooperative ramp merging mechanism is developed to facilitate ramp merging, and to properly capture the cooperative and non-cooperative behaviours in mixed traffic. The mechanism can be described as a bi-level optimization program in which the optimal control-based trajectory design problem is imbedded in the merge sequencing problem. A bi-level dynamic programming-based solution approach is developed to efficiently solve the problem. A micro-simulation environment is built for model validation and analysis of mixed traffic. The results show that cooperative ramp merging leads to larger throughput and smoother traffic.

关键词： Connected Automated Vehicles (CAVs) cooperative ramp merging bi-level optimization dynamic programming mixed traffic

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SHE: A Generic Framework for data Stream Mining over Sliding Windows 22

SHE: A Generic Framework for Data Stream Mining over Sliding...

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Proceedings of the 51st International Conference on Parallel Processing

作者： Yuhan Wu Zhuochen Fan Qilong Shi Yixin Zhang Tong Yang Cheng Chen Zheng Zhong Junnan Li Ariel Shtul Yaofeng Tu School of Computer Science and National Engineering Laboratory for Big Data Analysis Technology and Application Peking University China and PCL Research Center of Networks and Communications Peng Cheng Laboratory China School of Computer Science and National Engineering Laboratory for Big Data Analysis Technology and Application Peking University China National University of Defense Technology China Redis Labs Israel ZTE Corporation China

ISBN: (纸本)9781450397339

1data stream mining over a sliding window is a fundamental problem in many applications, such as financial data trackers, intrusion detection and QoS. To meet the demand for high throughput of high speed data streams, sliding window algorithms turn to hardware platforms including FPGA/ASIC and programmable switches. These hardware platforms have three constraints for algorithms running on, which are 1) small memory usage 2) single stage memory access and 3) limited concurrent memory access. Algorithms perfectly fit in with these constraints will enable a highest utilization of these hardware platforms. However, no existing sliding window algorithm is specifically designed for hardware platforms. In this paper, we propose the Sliding Hardware Estimator (SHE), which is a generic framework that extends existing fixed window algorithms to sliding windows on hardware platforms. The key idea of SHE is that, during insertions we approximately delete out-dated information with little time and space overhead, while during queries we design sophisticated techniques to minimize error. We have fully implemented our SHE on FPGA, achieving a throughput of 544 Mips. We apply SHE to four typical data stream mining tasks. Experimental results show that, when compared with the state-of-the-art which cannot be implemented in hardware, SHE reduces the error by up to 100 times in membership queries. All related source codes are released at Github.

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