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Monopulse transmitarray antenna fed by aperture-coupled microstrip structure

Frontiers of Information Technology & Electronic engineering 2022年第3期23卷 502-510页

作者： Na KOU Shixing YU Zhao DING Zhengping ZHANG College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Key Laboratory of Micro-Nano-Electronics and Software Technology ofGuizhou Province Guiyang 550025China Engineering Research Center of Power Semiconductor Device Reliability of the Ministry of Education Guiyang 550025China

An X-band monopulse antenna based on transmitarray with light-weight and low-cost features is designed,fabricated,and *** proposed antenna is composed of a transmitarray lens,a feed source which combines aperture-coupled microstrip antennas,and a sum-difference(SUM-DIFF)compar-atoi\According to simulation and measurement results of the transmitarray based monopulse antenna,gain of sum beam reaches 21.5 dBi.

关键词： antenna pulse transmit

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A comparative study of solving the surface area of solid figures in dynamic geometry system 16

A comparative study of solving the surface area of solid fig...

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16th IEEE International Conference on Computer Science and Education, ICCSE 2021

作者： Chen, Ningxin Guan, Hao Wang, Ying Chen, Ruxian Cai, Ruiqi Zhang, Chuanjun Institute of Computing Science and Technology Guangzhou University Guangzhou China Institute of Guangzhou Software Engineering Guangzhou China Guizhou Education University School of Mathematics and Big Data Guiyang China

ISBN: (纸本)9781665414685

As an important part of geometry, solid figures is difficult for learning and teaching. Three-dimensional dynamic geometry system(3D DGS) is significant to assist the teaching of solid figures. Solid figures can be edited and modified effectively in 3D DGS. 3D DGS provides rich functions such as viewpoint transformation, animation, text, etc., which display dynamic geometric figures more clearly and provide a vivid teaching situation in symbolic-graphic combination. We use Cabri 3D, GeoGebra and Netpad to assist in solving the problem of solid geometric surface area, and compare the pros and cons of the functions fo the three 3D DGS. We focus on how Netpad assists users in constructing and calculating the surface area of solid figures. © 2021 IEEE.

关键词： Geometry

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Shilling Black-box Recommender Systems by Learning to Generate Fake User Profiles

arXiv

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arXiv 2022年

作者： Lin, Chen Chen, Si Zeng, Meifang Zhang, Sheng Gao, Min Li, Hui School of Informatics Xiamen University Xiamen361005 China School of Big Data and Software Engineering Chongqing University Chongqing400044 China

Due to the pivotal role of Recommender Systems (RS) in guiding customers towards the purchase, there is a natural motivation for unscrupulous parties to spoof RS for profits. In this paper, we study Shilling Attack where an adversarial party injects a number of fake user profiles for improper purposes. Conventional Shilling Attack approaches lack attack transferability (i.e., attacks are not effective on some victim RS models) and/or attack invisibility (i.e., injected profiles can be easily detected). To overcome these issues, we present Leg-UP, a novel attack model based on the Generative Adversarial Network. Leg-UP learns user behavior patterns from real users in the sampled "templates" and constructs fake user profiles. To simulate real users, the generator in Leg-UP directly outputs discrete ratings. To enhance attack transferability, the parameters of the generator are optimized by maximizing the attack performance on a surrogate RS model. To improve attack invisibility, Leg-UP adopts a discriminator to guide the generator to generate undetectable fake user profiles. Experiments on benchmarks have shown that Leg-UP exceeds state-of-the-art Shilling Attack methods on a wide range of victim RS models. The source code of our work is available at: https://***/XMUDM/ShillingAttack. Copyright © 2022, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Analysis of Elderly Care Services Based on Machine Learning and Factor Rotation Method

Analysis of Elderly Care Services Based on Machine Learning ...

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Intelligent data and Security (IDS), IEEE International Conference on

作者： Shuyi Li Fangming Jing Chen Yang Pengyu Zhang Zeming Wang Wenhui Hu School of Software & Microelectronics Peking University Beijing China Information Center Ministry of Civil Affairs of the People's Republic of China Beijing China Big data center State Grid Corporation of China Beijing China Lake Forest Academy United States National Engineering Research Center for Software Engineering Peking University Beijing China

ISBN: (数字)9798350389487

ISBN: (纸本)9798350389494

As China's aging population continues to escalate, the societal burden of elderly care has notably intensified. Presently, the spectrum of elderly care services in China encompasses institutional, community-based, and home-based care. Given the diverse needs of the elderly population, this study proposes an Elderly Care Service Selection Predictive Model. It aims to forecast the probable choice of elderly care service types by analyzing various characteristics of the elderly individuals. Simultaneously, considering the status of institutional versus community-based elderly care across different provinces and municipalities, alongside the basic demographics of the elderly, this research scrutinizes the supply and demand scenario of elderly care services in each region, assessing the equilibrium between the two. By forecasting elderly care service preferences and evaluating supply-demand dynamics, this analysis contributes to aiding governmental agencies in more rationally allocating elderly care resources across regions, thus meeting the diverse needs of the aging population.

关键词： Supply and demand Sociology Machine learning Aging Predictive models Dynamic scheduling Security

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A Novel Class Imbalance-oriented Polynomial Neural Network Algorithm for Disease Diagnosis

A Novel Class Imbalance-oriented Polynomial Neural Network A...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiaohan Yuan Shuyu Chen Chuan Sun Lu Yuwen School of Big Data & Software Engineering Chongqing University Chongqing China

ISBN: (纸本)9781665429825

The class imbalance problem is common in disease diagnosis, which significantly damages the artificial intelligence-based diagnostic models and causes enormous cost about disease misclassification. To alleviate the class imbalance problem, we propose a novel class imbalance-oriented polynomial neural network (CIPNN) algorithm, which incorporates the data sampling and the classifier ensemble. Specifically, we utilize the nearest neighbor algorithm to identify a critical area from the given medical dataset (original dataset), which forms a critical area dataset. Then, we apply the synthetic minority over-sampling technique to generate a balanced dataset based on the original medical dataset. Further, we obtain the ensemble by combining multiple polynomial neural network classifiers, which are learned from the bootstrap samples sampled from the balanced dataset, the critical area dataset, and the original medical dataset, respectively. The experiments conducted on nine imbalanced medical datasets demonstrate that the proposed method can effectively alleviate the class imbalance problem.

关键词： Training Costs Conferences Neural networks Diversity reception Classification algorithms Medical diagnosis

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A new evidence reliability coefficient for conflict data fusion and its application in classification

A new evidence reliability coefficient for conflict data fus...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Shuaihong Wu Yongchuan Tang School of Big Data and Software Engineering Chongqing University Chongqing China

ISBN: (纸本)9781665442084

Information fusion-based classification is a key issue in many practical applications of automation. Dempster-Shafer evidence theory is a tool to model and fuse uncertain information. However, if there is a high contradiction between two bodies of evidence, the classical Dempster combination rule often gives the fusion result which violates the intuitive results. To address this issue, this paper proposes a single factor belief function with a new evidence reliability coefficient. The original evidence is preprocessed based on the reliability coefficient to get new evidence and to generate a reasonable basic probability assignment (BPA) function. After that, the combined results of the evidence are expressed in the binary form of reliability coefficient and BPA. The merit of the proposed method is that it takes advantage of the characteristics of the evidence itself to deal with the uncertain data and avoids the problem that the classical Dempster combination rule is not applicable for high conflict evidence. Finally, two UCI data sets are used to verify the rationality and effectiveness of the new method.

关键词： Fault tolerance Fuses Evidence theory Fault tolerant systems Decision making data integration Reliability theory

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(Rectified Version) The Barzilai-Borwein Method for Distributed Optimization over Unbalanced Directed Networks

arXiv

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arXiv 2023年

作者： Hu, Jinhui Chen, Xin Zheng, Lifeng Zhang, Ling Li, Huaqing Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China School of Big Data & Software Engineering Chongqing University Chongqing401331 PR China

This paper studies optimization problems over multi-agent systems, in which all agents cooperatively minimize a global objective function expressed as a sum of local cost functions. Each agent in the systems uses only local computation and communication in the overall process without leaking their private information. Based on the Barzilai-Borwein (BB) method and multi-consensus inner loops, a distributed algorithm with the availability of larger step-sizes and accelerated convergence, namely ADBB, is proposed. Moreover, owing to employing only row-stochastic weight matrices, ADBB can resolve the optimization problems over unbalanced directed networks without requiring the knowledge of neighbors’ out-degree for each agent. Via establishing contraction relationships between the consensus error, the optimality gap, and the gradient tracking error, ADBB is theoretically proved to converge linearly to the globally optimal solution. A real-world data set is used in simulations to validate the correctness of the theoretical analysis. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi agent systems

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Circular Decomposition and Cross-Modal Recombination for Multimodal Sentiment Analysis

Circular Decomposition and Cross-Modal Recombination for Mul...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Haijian Liang Weicheng Xie Xilin He Siyang Song Linlin Shen School of Computer Science & Software Engineering Computer Vision Institue Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University School of Computing and Mathematical Sciences University of Leicester National Engineering Laboratory for Big Data System Computing Technology Shenzhen University

Multimodal Sentiment Analysis is a burgeoning research area, leveraging various modalities to predict the sentiment score. Nevertheless, previous studies have disregarded the impact of noise interference on specific modal sentiments during video recording, thereby compromising the accuracy of sentiment prediction. In this paper, we propose the Guided Circular Decomposition and Cross-Modal Recombination (GCD-CMR) model, which aims to eliminate contaminated sentiment features in a fine-grained way. To achieve this, we utilize tailored global information specific to each modality to guide the circular decomposing process in the GCD module, to produce a set of sentiment prototypes. Subsequently, in the CMR module, we align cross-modal sentiment prototypes and remove the contaminated prototypes for recombination. Experimental results on two publicly available datasets demonstrate that our model surpasses state-of-the-art models, confirming the effectiveness of our proposed method. We release the code at: https://***/nianhua20/GCD-CMR.

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Multi-Server Cooperative Video Caching Strategy Based on Deep Reinforcement Learning in Cloud-Edge Computing

Multi-Server Cooperative Video Caching Strategy Based on Dee...

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International Conference on big data Computing and Communications (bigCOM)

作者： Guanyu Li Yingchi Mao XinXin Peng Haotian Zheng Zibo Wang College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China Huaneng Hydro Lancang Xiaowan Hydropower Plant Dali China

ISBN: (数字)9798331509538

ISBN: (纸本)9798331509545

The soaring number of mobile devices has led the innovation of the edge caching in edge computing, which relieves the heavy pressure of cloud computing. However, with the escalating popularity of short video services, the prevalence of video content in edge server caches has seen a marked increase. Previous work ignored the impact of edge server performance on video caching and failed to automatically adjust the optimal video bit rate in response to users' demand. To improve users' evaluation for watching videos on devices with different display sizes, we propose a novel edge caching strategy called Gate Self-attention Recurrent Network-Multi Agent Reinforcement Learning(GSRN-MARL). GSRN-MARL utilizes leverages Gate Recurrent Unit(GRU) and attention mechanisms to predict cache content based on user historical request information and device details, consequently improving edge server caching performance and users' evaluation of video. Experiment demonstrates that GSRN-MARL surpasses LRU, DRL, and MAAC across two critical metrics: video cache hit rate and users' QoE (Quality of Experience).

关键词： Performance evaluation Training Cloud computing Technological innovation Bit rate Logic gates Mobile handsets Servers Quality of experience Edge computing

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Forecasting of credit card default based on incremental random forest

Forecasting of credit card default based on incremental rand...

引用

2020 International Symposium on Automation, Information and Computing, ISAIC 2020

作者： Gao, Xiang Wen, Junhao Zhang, Cheng College of Computer Science Chongqing University Chongqing China College of Big Data and Software Engineering Chongqing University Chongqing China Chongqing Branch of China Minsheng Bank Chongqing China

In this paper, the incremental random forest algorithm is proposed for the classification and prediction problem of dynamically increasing data. Traditional batch machine learning algorithms perform modeling at one time and cannot allow newly generated samples to participate in learning, which leads to too much model deviation. This paper combines incremental learning with random forest and proposes incremental random forest. Applying this algorithm to the problem of predicting credit card customer default behavior can help banks control risks and reduce losses. It is important to conduct card issuance audits on card issuers and early warning of risks to cardholders. The algorithm performed better in the experiment of predicting the default behavior of credit card customers based on a batch of credit card holder data of a bank in Taiwan. Compared with random forest, decision tree, logistic regression, naive bayes, BP neural network, and support vector machine, it has relatively better performance in our experiment. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Random forests

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