检索结果-南通市图书馆

Structural evolution and high-temperature sensing performance of polymer-derived SiAlBCN ceramics

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 Advanced Ceramics 2024年第4期13卷 478-485页

作者： Chao Ma Kun Liu Pengfei Shao Daoyang Han Kang Wang Mengmeng Yang Rui Zhao Hailong Wang Rui Zhang Gang Shao school of Materials Science and Engineering Zhengzhou UniversityZhengzhou 450001China Zhongyuan Critical Metal Laboratory Zhengzhou UniversityZhengzhou 450001China State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentZhengzhou UniversityZhengzhou 450002China

In situ temperature monitoring has become extremely imperative in high-temperature harsh environments and polymer-derived ceramics(PDCs)as sensing materials have attracted great ***,the stability and oxidation/corrosion resistance of PDCs cannot be simultaneously achieved at the moment,limiting their practical ***,polymer-derived SiAlBCN ceramics were synthesized via polymer conversion method under different pyrolysis *** microstructure evolution,high temperature sensing properties,and stability were investigated in *** results show that the amorphous SiAlBCN phase grows more orderly and the size of the free carbon phase enlarges with the increasing *** defect concentration displays a decreasing ***,the SiAlBCN ceramics as sensing materials exhibit a good temperature-resistance property from roo temperature to 1100℃.The fabricated SiAlBCN temperature sensor possesses excellent stability,repeatability,and ***,SiAlBCN ceramics exhibit distinguished oxidation/corrosion resistance after 100 h treatment at 12.0℃in a water/oxygen environment,which is attributed to their low corrosive rate constant(0.57 mg/(cm^(2.·h))and oxidative rate constant(3.43 mg^(2./(cm^(4)·h)).Therefore,polymer-derived SiAlBCN ceramics as sensing materials,which possess outstanding stability and oxidation/corrosion resistance,have great potential for in-situ monitoring of extreme environmental temperatures in the future.

关键词： polymer-derived SiAlBCN ceramics temperature sensor harsh environments oxidation and corrosion resistance

SMEC:Scene Mining for E-Commerce

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 Computer Science & Technology 2024年第1期39卷 192-210页

作者：王罡李翔郭子义殷大伟马帅 State Key Laboratory of software Development Environment Beihang UniversityBeijing 100191China school of Data Science and Engineering East China Normal UniversityShanghai 200062China *** Inc.Beijing 100176China Baidu Inc.Beijing 100085China

Scene-based recommendation has proven its usefulness in E-commerce,by recommending commodities based on a given ***,scenes are typically unknown in advance,which necessitates scene discovery for *** this article,we study scene discovery for E-commerce *** first formalize a scene as a set of commodity cate-gories that occur simultaneously and frequently in real-world situations,and model an E-commerce platform as a heteroge-neous information network(HIN),whose nodes and links represent different types of objects and different types of rela-tionships between objects,*** then formulate the scene mining problem for E-commerce as an unsupervised learning problem that finds the overlapping clusters of commodity categories in the *** solve the problem,we pro-pose a non-negative matrix factorization based method SMEC(Scene Mining for E-Commerce),and theoretically prove its *** six real-world E-commerce datasets,we finally conduct an extensive experimental study to evaluate SMEC against 13 other methods,and show that SMEC consistently outperforms its competitors with regard to various evaluation measures.

关键词： graph clustering E-commerce heterogeneous information network(HIN) scene mining

A Fast Algorithm for Computing the Deficiency Number of a Mahjong Hand

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Chinese Journal of Electronics 2024年

作者： Xueqing YAN Yongming LI Sanjiang LI school of Computer Science Shaanxi Normal University school of Mathematics and Statistics Shaanxi Normal University Centre for Quantum software and Information Faculty of Engineering and Information Technology University of Technology Sydney

The tile-based multiplayer game Mahjong is widely played in Asia and has also become increasingly popular worldwide. Face-to-face or online, each player begins with a hand of 13 tiles and players draw and discard tiles in turn until they complete a winning hand. An important notion in Mahjong is the deficiency number(*** number in Japanese Mahjong) of a hand, which estimates how many tile changes are necessary to complete the hand into a winning hand. The deficiency number plays an essential role in major decision-making tasks such as selecting a tile to discard. This paper proposes a fast algorithm for computing the deficiency number of a Mahjong hand. Compared with the baseline algorithm, the new algorithm is usually 100 times faster and, more importantly,respects the agent's knowledge about available tiles. The algorithm can be used as a basic procedure in all Mahjong variants by both rule-based and machine learning-based Mahjong AI.

关键词：

FedQMIX:Communication-efficient federated learning via multi-agent reinforcement learning

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

High-Confidence Computing

High-Confidence Computing 2024年第2期4卷 96-104页

作者： Shaohua Cao Hanqing Zhang Tian Wen Hongwei Zhao Quancheng Zheng Weishan Zhang Danyang Zheng Qingdao Institute of software College of Computer Science and TechnologyChina University of Petroleum(East China)Qingdao 266580China school of Computing and Artificial Intelligence Xipu CampusSouthwest Jiaotong UniversityChengdu 611756China

Since the data samples on client devices are usually non-independent and non-identically distributed(non-IID),this will challenge the convergence of federated learning(FL)and reduce communication *** paper proposes FedQMIX,a node selection algorithm based on multi-agent reinforcement learning(MARL),to address these ***,we observe a connection between model weights and data distribution,and a clustering algorithm can group clients with similar data distribution into the same ***,we propose a QMIX-based mechanism that learns to select devices from clustering results in each communication round to maximize the reward,penalizing the use of more communication rounds and thereby improving the communication efficiency of ***,experiments show that FedQMIX can reduce the number of communication rounds by 11%and 30%on the MNIST and CIFAR-10 datasets,respectively,compared to the baseline algorithm(Favor).

关键词： Communication efficient Federated learning MARL

Image super‐resolution via dynamic network

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

CAAI Transactions on Intelligence Technology 2024年第4期9卷 837-849页

作者： Chunwei Tian Xuanyu Zhang Qi Zhang Mingming Yang Zhaojie Ju school of software Northwestern Polytechnical UniversityXi'anChina National Engineering Laboratory for Integrated Aero‐Space‐Ground‐Ocean Big Data Application Technology Xi'anChina Research&Development Institute Northwestern Polytechnical UniversityShenzhenChina school of Economics and Management Harbin Institute of Technology at WeihaiWeihaiChina Tencent AI Lab ShenzhenChina school of Computing University of PortsmouthPortsmouthUK

Convolutional neural networks depend on deep network architectures to extract accurate information for image super‐***,obtained information of these con-volutional neural networks cannot completely express predicted high‐quality images for complex scenes.A dynamic network for image super‐resolution(DSRNet)is presented,which contains a residual enhancement block,wide enhancement block,feature refine-ment block and construction *** residual enhancement block is composed of a residual enhanced architecture to facilitate hierarchical features for image super‐*** enhance robustness of obtained super‐resolution model for complex scenes,a wide enhancement block achieves a dynamic architecture to learn more robust information to enhance applicability of an obtained super‐resolution model for varying *** prevent interference of components in a wide enhancement block,a refine-ment block utilises a stacked architecture to accurately learn obtained ***,a residual learning operation is embedded in the refinement block to prevent long‐term dependency ***,a construction block is responsible for reconstructing high‐quality *** heterogeneous architecture can not only facilitate richer structural information,but also be lightweight,which is suitable for mobile digital *** results show that our method is more competitive in terms of performance,recovering time of image super‐resolution and *** code of DSRNet can be obtained at https://***/hellloxiaotian/DSRNet.

关键词： CNN dynamic network image super‐resolution lightweight network

Virtual Assembly Collision Detection Algorithm Using Backpropagation Neural Network

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials and Continua 2024年第1期81卷 1085-1100页

作者： Wang, Baowei You, Wen school of Computer Nanjing University of Information Science and Technology Collaborative Innovation Center of Jiangsu Atmospheric Environment and Equipment Technology Digital Forensics Engineering Research Center of Digital Forensics Ministry of Education Nanjing 210044 China school of software Nanjing University of Information Science and Technology Nanjing 210044 China

As computer graphics technology continues to advance, Collision Detection (CD) has emerged as a critical element in fields such as virtual reality, computer graphics, and interactive simulations. CD is indispensable for ensuring the fidelity of physical interactions and the realism of virtual environments, particularly within complex scenarios like virtual assembly, where both high precision and real-time responsiveness are imperative. Despite ongoing developments, current CDtechniques often fall short inmeeting these stringent requirements, resulting in inefficiencies and inaccuracies that impede the overall performance of virtual assembly systems. To address these limitations, this study introduces a novel algorithm that leverages the capabilities of a Backpropagation Neural Network (BPNN) to optimize the structural composition of the Hybrid Bounding Volume Tree (HBVT). Through this optimization, the research proposes a refinedHybridHierarchical Bounding Box (HHBB) framework, which is specifically designed to enhance the computational efficiency and precision ofCDprocesses. TheHHBBframework strategically reduces the complexity of collision detection computations, thereby enabling more rapid and accurate responses to collision events. Extensive experimental validation within virtual assembly environments reveals that the proposed algorithm markedly improves the performance of CD, particularly in handling complex models. The optimized HBVT architecture not only accelerates the speed of collision detection but also significantly diminishes error rates, presenting a robust and scalable solution for real-time applications in intricate virtual systems. These findings suggest that the proposed approach offers a substantial advancement in CD technology, with broad implications for its application in virtual reality, computer graphics, and related fields. © 2.2. The Authors.

关键词： Virtual environments

IPFA-Net: Important Points Feature Aggregating Net for Point Cloud Classification and Segmentation

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Chinese Journal of Electronics 2024年

作者： Jingya WANG Yu ZHANG Bin ZHANG Jinxiang XIA Weidong WANG school of Information and software Engineering University of Electronic Science and Technology of China National Key Laboratory of Automotive Chassis Integration and Bionics Jilin University

This paper focuses on the problems of point cloud deep neural networks in classification and segmentation tasks, including losing important information during down-sampling, ignoring relationships among points when extracting features, and network performance being susceptible to the sparsity of point cloud. To begin with, this paper proposes a farthest point sampling(FPS)-important points sampling(F-IPS) method for down-sampling, which can preserve important information of point clouds and maintain the geometry of input data. Then, the local feature relation aggregating(LFRA) method is proposed for feature extraction, improving the network's ability to learn contextual information and extract rich local region features. Based on these methods, the important points feature aggregating net(IPFA-Net) is designed for point cloud classification and segmentation tasks. Furthermore, this paper proposes the multi-scale multi-density feature connecting(MMFC) method to reduce the negative impact of point cloud data sparsity on network performance. Finally, the effectiveness of IPFA-Net is demonstrated through experiments on ModelNet40, ShapeNet part, and ScanNet v2.datasets. IPFA-Net is robust to reducing the number of point clouds, with only a 3.3% decrease in accuracy under a 16-fold reduction of point number. In the part segmentation experiments, our method achieves the best segmentation performance for five objects.

关键词：

Boosting Adversarial Training with Learnable Distribution

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2024年第3期78卷 3247-3265页

作者： Kai Chen Jinwei Wang James Msughter Adeke Guangjie Liu Yuewei Dai school of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing210044China Key Laboratory of Intelligent Support Technology for Complex Environments Ministry of EducationNanjing210044China school of Computer and software Nanjing University of Information Science and TechnologyNanjing210044China Nanjing Center for Applied Mathematics Nanjing211135China

In recent years,various adversarial defense methods have been proposed to improve the robustness of deep neural *** training is one of the most potent methods to defend against adversarial ***,the difference in the feature space between natural and adversarial examples hinders the accuracy and robustness of the model in adversarial *** paper proposes a learnable distribution adversarial training method,aiming to construct the same distribution for training data utilizing the Gaussian mixture *** distribution centroid is built to classify samples and constrain the distribution of the sample *** natural and adversarial examples are pushed to the same distribution centroid to improve the accuracy and robustness of the *** proposed method generates adversarial examples to close the distribution gap between the natural and adversarial examples through an attack algorithm explicitly designed for adversarial *** algorithm gradually increases the accuracy and robustness of the model by scaling ***,the proposed method outputs the predicted labels and the distance between the sample and the distribution *** distribution characteristics of the samples can be utilized to detect adversarial cases that can potentially evade the model *** effectiveness of the proposed method is demonstrated through comprehensive experiments.

关键词： Adversarial training feature space learnable distribution distribution centroid

Large language model and domain-specific model collaboration for smart education

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Frontiers of Information Technology & Electronic Engineering 2024年第3期25卷 333-341页

作者： Yawei LUO Yi YANG school of software Technology Zhejiang UniversityNingbo 315048China College of Computer Science and Technology Zhejiang UniversityHangzhou 310027China

In this paper,we introduce the large language model and domain-specific model collaboration(LDMC)framework designed to enhance smart *** LDMC framework leverages the comprehensive and versatile knowledge of large domain-general models,combines it with the specialized and disciplinary knowledge from small domainspecific models(DSMs),and incorporates pedagogy knowledge from learning theory *** integration yields multiple knowledge representations,fostering personalized and adaptive educational *** explore various applications of the LDMC framework in the context of smart education.

关键词： smart language knowledge