检索结果-南通市图书馆

维普期刊数据库

Single image defogging via multi-exposure image fusion and detail enhancement

在线全文

学校读者我要写书评

暂无评论

Journal of Safety Science and Resilience 2024年第1期5卷 37-46页

作者： Wenjing Mao Dezhi Zheng Minze Chen Juqiang Chen School of Information and Electronics Advanced Research Institute of Multidisciplinary ScienceBeijing Institute of TechnologyBeijing100081China Chinese aeronautical Establishment Beijing100012China

Outdoor cameras play an important role in monitoring security and social governance.As a common weather phenomenon,haze can easily affect the quality of camera shooting,resulting in loss and distortion of image details.This paper proposes an improved multi-exposure image fusion defogging technique based on the artificial multi-exposure image fusion(AMEF)algorithm.First,the foggy image is adaptively exposed,and the fused image is subsequently obtained via multiple exposures.The fusion weight is determined by the saturation,contrast,and brightness.Finally,the image fused by a multi-scale Laplacian algorithm is enhanced with simple adaptive details to obtain a clearer defogging image.It is subjectively and objectively verified that this algorithm can obtain more image details and distinct picture colors without a priori information,effectively improving the defogging ability.

关键词： Image defogging Multi-scale fusion Laplacian pyramid Adaptive detail enhancement

Microstructure evolution and phase transformation of the mushy zone in a quenchedβ-solidifying TiAl alloy

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Materials Science & Technology 2024年第2期169卷 28-41页

作者： Fuqiang Zhang Xianfei Ding Leming Xu Xin Feng Hai Nan Jianping He Yongfeng Liang Junpin Lin State Key Laboratory for Advanced Metals and Materials University of Science and Technology BeijingBeijing 100083China Cast Titanium Alloy R&D Center Beijing Institute of Aeronautical MaterialsBeijing 100095China

This study investigates the phase constitutions and transformations that occur in the mushy zone and in the adjacent phase fields of a directionally solidified Ti-44Al-8Nb-1Cr alloy via quenching technique.The results indicate that the mushy zone consists of unmeltedβdendrites and interdendritic liquid,whose formation can be attributed to the difference in melting point aroused by local heterogeneity in solutecontent.Theβdendrite is composed of numerous subgrains with various orientations.During quenching,theβdendrite transforms into Widmanstättenαvia a precipitation reaction,owing to the decreasing cooling rate caused by heat transfer from the surrounding liquid.Additionally,after quenching,the interdendritic liquid is transformed intoγplates.Within the singleβphase field and the lower part of the mushy zone,a massive transformation ofβtoγoccurs.Conversely,in theβ+αphase field,bothβandαphases are retained to ambient temperature.During the heating process,the transformation ofα→βgives rise to the formation ofβvariants,which affects the orientation ofβdendrites in the mushy zone.The growth kinematics of theα→βtransformation was elucidated,revealing the preferential growth directions of111and112forβvariants.Furthermore,this study presents an illustration of the formation process of the mushy zone and the microstructural evolution during the heating and quenching process.

关键词： Titanium aluminides Mushy zone Quenching Electron backscattering diffraction Phase transformation

XRL-SHAP-Cache:an explainable reinforcement learning approach for intelligent edge service caching in content delivery networks

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Science China(Information Sciences) 2024年第7期67卷 46-71页

作者： Xiaolong XU Fan WU Muhammad BILAL Xiaoyu XIA Wanchun DOU Lina YAO Weiyi ZHONG School of Software Nanjing University of Information Science and Technology School of computing and Communications Lancaster University School of computing Technologies Royal Melbourne Institute of Technology State Key Laboratory for Novel Software Technology Nanjing University School of Computer Science and Engineering University of New South Wales Data 61 Commonwealth Scientific and Industrial Research Organization School of Computer Science Qufu Normal University

Content delivery networks(CDNs) play a pivotal role in the modern internet infrastructure by enabling efficient content delivery across diverse geographical regions. As an essential component of CDNs, the edge caching scheme directly influences the user experience by determining the caching and eviction of content on edge servers. With the emergence of 5G technology, traditional caching schemes have faced challenges in adapting to increasingly complex and dynamic network environments. Consequently, deep reinforcement learning(DRL) offers a promising solution for intelligent zero-touch network governance. However, the blackbox nature of DRL models poses challenges in understanding and making trusting decisions. In this paper,we propose an explainable reinforcement learning(XRL)-based intelligent edge service caching approach,namely XRL-SHAP-Cache, which combines DRL with an explainable artificial intelligence(XAI) technique for cache management in CDNs. Instead of focusing solely on achieving performance gains, this study introduces a novel paradigm for providing interpretable caching strategies, thereby establishing a foundation for future transparent and trustworthy edge caching solutions. Specifically, a multi-level cache scheduling framework for CDNs was formulated theoretically, with the D3QN-based caching scheme serving as the targeted interpretable model. Subsequently, by integrating Deep-SHAP into our framework, the contribution of each state input feature to the agent's Q-value output was calculated, thereby providing valuable insights into the decision-making process. The proposed XRL-SHAP-Cache approach was evaluated through extensive experiments to demonstrate the behavior of the scheduling agent in the face of different environmental inputs.The results demonstrate its strong explainability under various real-life scenarios while maintaining superior performance compared to traditional caching schemes in terms of cache hit ratio, quality of service(QoS),and space utilization.

关键词： deep reinforcement learning (DRL) explainable artificial intelligence (XAI) multi-level cache content delivery network (CDN) D3QN algorithm Deep-SHAP

Laser Wire Additive Manufacturing of Ti-6Al-4V Alloy and Its Machine Learning Study for Parameters Optimization(Invited)

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

中国激光 2024年第4期51卷 182-198页

作者： Wu Junyi Zhang Bo Wang Weihua Li Weipeng Yao Xiyu Wang Dawei Xing Wei Yan Ming Department of Materials Science and Engineering Southern University of Science and TechnologyShenzhen 518055GuangdongChina Songshan Lake Materials Laboratory Dongguan 523830GuangdongChina institute of Physics Chinese Academy of SciencesBeijing 100190China Jiaxing research institute Southern University of Science and TechnologyShenzhen 518055GuangdongChina High Performance computing Department National Supercomputing CenterShenzhen 518055GuangdongChina

Ti-6Al-4V is a benchmark Ti alloy. Laser wire additive manufacturing(LWAM) offers advanced manufacturing capability to the alloy for applications possibly including exploration of outer space. As a typical multiple-variable process, LWAM is complex, which, however, can be analyzed, predicated or even optimized by artificial intelligence(AI) methods such as machine learning(ML). In this study, printing parameters of the Ti-6Al-4V is firstly optimized using single-track-single-layer experiments, and then single-track-multiple-layer samples are printed, whose properties in terms of hardness and compressive strength are analyzed subsequently by both experiments and ML. The two ML approaches, artificial neural network(ANN) and support vector machine(SVM), are employed to predict the experimental results, whose coefficients of determination R2 show good values. Further optimized properties are realized by adopting genetic algorithm(GA) and simulated annealing(SA) approaches, which contribute to high mechanical properties achieved, for instance, an engineering compressive strength of about 1694 MPa. The results here indicate that important mechanical properties of the LWAM-prepared Ti alloys can be well predicted and enhanced using suitable ML approaches.

关键词： artificial printing additive

维普期刊数据库

Falcon Optimization Algorithm-Based Energy Efficient Communication Protocol for Cluster-Based Vehicular Networks

在线全文

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2024年第3期78卷 4243-4262页

作者： Youseef Alotaibi B.Rajasekar R.Jayalakshmi Surendran Rajendran Department of Software Engineering College of ComputingUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Electronics and Communication Engineering Sathyabama Institute of Science and TechnologyChennai600119India Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical ScienceChennai602105India

Rapid development in Information Technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)transmission.Vehicular networks give a safe and more effective driving experience by presenting time-sensitive and location-aware data.The communication occurs directly between V2V and Base Station(BS)units such as the Road Side Unit(RSU),named as a Vehicle to Infrastructure(V2I).However,the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with time.Therefore,the scheme of an effectual routing protocol for reliable and stable communications is significant.Current research demonstrates that clustering is an intelligent method for effectual routing in a mobile environment.Therefore,this article presents a Falcon Optimization Algorithm-based Energy Efficient Communication Protocol for Cluster-based Routing(FOA-EECPCR)technique in VANETS.The FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the VANET.To accomplish this,the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy,distance,and trust level.For the routing process,the Sparrow Search Algorithm(SSA)is derived with a fitness function that encompasses two variables,namely,energy and distance.A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR method.The experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.

关键词： Vehicular networks communication protocol clustering falcon optimization algorithm routing

Social Media-Based Surveillance Systems for Health Informatics Using Machine and Deep Learning techniques:A Comprehensive Review and Open Challenges

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computer Modeling in Engineering & Sciences 2024年第5期139卷 1167-1202页

作者： Samina Amin Muhammad Ali Zeb Hani Alshahrani Mohammed Hamdi Mohammad Alsulami Asadullah Shaikh institute of computing Kohat University of Science and TechnologyKohat26000Pakistan Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Information System College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia

Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic outbreaks.This review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their performance.Since,every year,a large amount of data related to epidemic outbreaks,particularly Twitter data is generated by SM.This paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM,along with the ML and DL techniques that have been configured for the detection of epidemic outbreaks.DL has emerged as a promising ML technique that adaptsmultiple layers of representations or features of the data and yields state-of-the-art extrapolation results.In recent years,along with the success of ML and DL in many other application domains,both ML and DL are also popularly used in SM analysis.This paper aims to provide an overview of epidemic outbreaks in SM and then outlines a comprehensive analysis of ML and DL approaches and their existing applications in SM analysis.Finally,this review serves the purpose of offering suggestions,ideas,and proposals,along with highlighting the ongoing challenges in the field of early outbreak detection that still need to be addressed.

关键词： Social media epidemic machine learning deep learning health informatics pandemic

COARSE-MESH-ACCURACY IMPROVEMENT OF BILINEAR Q_4-PLANE ELEMENT BY THE COMBINED HYBRID FINITE ELEMENT METHOD

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Applied Mathematics and Mechanics(English Edition) 2003年第12期24卷 1456-1465页

作者：谢小平周天孝 Mathematical College Sichuan University aeronautical computing technique research institute

The combined hybrid finite element method is of an intrinsic mechanism of enhancing coarse-mesh-accuracy of lower order displacement schemes. It was confirmed that the combined hybrid scheme without energy error leads to enhancement of accuracy at coarse meshes, and that the combination parameter plays an important role in the enhancement. As an improvement of conforming bilinear Q(4)-plane element, the combined hybrid method adopted the most convenient quadrilateral displacements-stress mode, i.e.,the mode of compatible isoparametric bilinear displacements and pure constant stresses. By adjusting the combined parameter, the optimized version of the combined hybrid element was obtained and numerical tests indicated that this parameter-adjusted version behaves much better than Q(4)-element and is of high accuracy at coarse meshes. Due to elimination of stress parameters at the elemental level, this combined hybrid version is of the same computational cost as that of Q(4)-element.

关键词： finite element hybrid method zero energy-error coarse-mesh-accuracy

维普期刊数据库

A Transfer Function Design for Medical Volume Data Using a Knowledge Database Based on Deep Image and Primitive Intensity Profile Features Retrieval

在线全文

学校读者我要写书评

暂无评论

Journal of Computer Science & Technology 2024年第2期39卷 320-335页

作者： Younhyun Jung Jim Kong 盛斌 Jinman Kim School of computing Gachon UniversitySeongnam 13120Korea School of Computer Science The University of SydneySydney 2006Australia Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China

Direct volume rendering(DVR)is a technique that emphasizes structures of interest(SOIs)within a volume visually,while simultaneously depicting adjacent regional information,e.g.,the spatial location of a structure concerning its neighbors.In DVR,transfer function(TF)plays a key role by enabling accurate identification of SOIs interactively as well as ensuring appropriate visibility of them.TF generation typically involves non-intuitive trial-and-error optimization of rendering parameters,which is time-consuming and inefficient.Attempts at mitigating this manual process have led to approaches that make use of a knowledge database consisting of pre-designed TFs by domain experts.In these approaches,a user navigates the knowledge database to find the most suitable pre-designed TF for their input volume to visualize the SOIs.Although these approaches potentially reduce the workload to generate the TFs,they,however,require manual TF navigation of the knowledge database,as well as the likely fine tuning of the selected TF to suit the input.In this work,we propose a TF design approach,CBR-TF,where we introduce a new content-based retrieval(CBR)method to automatically navigate the knowledge database.Instead of pre-designed TFs,our knowledge database contains volumes with SOI labels.Given an input volume,our CBR-TF approach retrieves relevant volumes(with SOI labels)from the knowledge database;the retrieved labels are then used to generate and optimize TFs of the input.This approach largely reduces manual TF navigation and fine tuning.For our CBR-TF approach,we introduce a novel volumetric image feature which includes both a local primitive intensity profile along the SOIs and regional spatial semantics available from the co-planar images to the profile.For the regional spatial semantics,we adopt a convolutional neural network to obtain high-level image feature representations.For the intensity profile,we extend the dynamic time warping technique to address subtle alignment differences between similar profiles(SOIs).Final

关键词： data retrieval direct volume rendering ray analysis transfer function

Non-intrusive Load Monitoring Based on Graph Total Variation for Residential Appliances

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Modern Power Systems and Clean Energy 2024年第3期12卷 947-957页

作者： Xiaoyang Ma Diwen Zheng Xiaoyong Deng Ying Wang Dawei Deng Wei Li the College of Electrical Engineering Sichuan University State Grid Chongqing Electric Power Company State Grid Sichuan Electric Power Company State Grid Hubei Electric Power Company Limited research institute

Non-intrusive load monitoring is a technique for monitoring the operating conditions of electrical appliances by collecting the aggregated electrical information at the household power inlet. Despite several studies on the mining of unique load characteristics, few studies have extensively considered the high computational burden and sample training. Based on lowfrequency sampling data, a non-intrusive load monitoring algorithm utilizing the graph total variation(GTV) is proposed in this study. The algorithm can effectively depict the load state without the need for prior training. First, the combined Kmeans clustering algorithm and graph signals are used to build concise and accurate graph structures as load models. The GTV representing the internal structure of the graph signal is introduced as the optimization model and solved using the augmented Lagrangian iterative algorithm. The introduction of the difference operator reduces the computing cost and addresses the inaccurate reconstruction of the graph signal. With low-frequency sampling data, the algorithm only requires a little prior data and no training, thereby reducing the computing cost. Experiments conducted using the reference energy disaggregation dataset and almanac of minutely power dataset demonstrated the stable superiority of the algorithm and its low computational burden.

关键词： Load monitoring Costs Home appliances Hidden Markov models Training Kernel Clustering algorithms