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作者： Jingchang Nan Yixin Wang Lu Zheng Longyou Zhao School of Electronic and Information Engineering Liaoning Technical University Key Laboratory of Radio Frequency and Big Data for Intelligent Applications Liaoning Technical University

In order to effectively alleviate the current situation of low-frequency spectrum congestion and meet people's demand for high-speed communication, a four element multiple input multiple output(MIMO) millimeter wave antenna was designed for the n260 frequency band(37-40GHz), with an overall size of 10mm×12mm× 0.508mm. This antenna is based on a dipole broadband millimeter wave unit antenna and adopts a coplanar waveguide feeding method. The four element antenna is placed orthogonally to improve the isolation of the antenna. The operating bandwidth is 36.76-41.73GHz, fully covering the n260 frequency band of 5G, with a relative bandwidth of 12.7%. The simulation results show that the impedance bandwidth of each port of the antenna covers this frequency band, and the isolation degree between each antenna unit port is less than-15dB. The envelope correlation coefficient(ECC)is less than 0.05, and has good radiation characteristics, which meets the performance requirements of MIMO antenna and can ensure the normal operation of the antenna.

关键词： millimeter wave antenna n260 Frequency-band 5G communication MIMO

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A Multi-Stream Structure-Enhanced Network for Mesh Denoising

A Multi-Stream Structure-Enhanced Network for Mesh Denoising

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IEEE International Conference on Systems, Man and Cybernetics

作者： Yutian Yang Lingyu Liang Jie Yan Yong Xu South China University of Technology Guangzhou China Henan Key Laboratory of Chronic Disease Prevention and Therapy & Intelligent Health Management Henan China Guangdong Provincial Key Laboratory of Multimodal Big Data Intelligent Analysis China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Triangular meshes provide an efficient representation of 3D shapes. Various applications such as 3D simulation suffer from degradation in geometric quality. This paper proposes a novel Multi-stream Structure-Enhanced Network (MSE-Net) based on graph convolutional networks. The network uses multi-scale features besides vertex position to guide face normal filtering, which can better preserve the geometric feature during the denoising process. In contrast to former methods that focus on filtering vertex coordinate and face normal apart, MSE-Net innovatively fuses more structure features like face area, inner product between face normal and vertex normals, and the interior angles of face to guide the face normal and vertex position updating, utilizing the inherent structural characteristic of Mesh. Our method achieves state-of-the-art performance on several publicly available datasets, demonstrating its effectiveness.

关键词： Degradation Three-dimensional displays Filtering Shape Graph convolutional networks Fuses Noise reduction Faces Cybernetics

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Variable projection algorithms with sparse constraint for separable nonlinear models

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Control Theory and Technology 2024年第1期22卷 135-146页

作者： Hui-Lang Xu Guang-Yong Chen Si-Qing Cheng Min Gan Jing Chen College of Computer and Data Science Fuzhou UniversityFuzhou350116FujianChina Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhou350116FujianChina Key Laboratory of Intelligent Metro of Universities in Fujian Fuzhou UniversityFuzhou350116FujianChina Engineering Research Center of Big Data Intelligence Ministry of EducationFuzhou UniversityFuzhou350116FujianChina School of Science Jiangnan UniversityWuxi214122JiangsuChina

Separable nonlinear models are widely used in various fields such as time series analysis, system modeling, and machine learning, due to their flexible structures and ability to capture nonlinear behavior of data. However, identifying the parameters of these models is challenging, especially when sparse models with better interpretability are desired by practitioners. Previous theoretical and practical studies have shown that variable projection (VP) is an efficient method for identifying separable nonlinear models, but these are based on \(L_2\) penalty of model parameters, which cannot be directly extended to deal with sparse constraint. Based on the exploration of the structural characteristics of separable models, this paper proposes gradient-based and trust-region-based variable projection algorithms, which mainly solve two key problems: how to eliminate linear parameters under sparse constraint;and how to deal with the coupling relationship between linear and nonlinear parameters in the model. Finally, numerical experiments on synthetic data and real time series data are conducted to verify the effectiveness of the proposed algorithms.

关键词： Variable projection(VP) Non-smooth constraint Separable nonlinear models

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Graph convolutional network for difficulty-controllable visual question generation

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WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS 2023年第6期26卷 3735-3757页

作者： Chen, Feng Xie, Jiayuan Cai, Yi Lin, Zehang Li, Qing Wang, Tao South China Univ Technol Sch Software Engn Guangzhou Peoples R China South China Univ Technol Minist Educ Key Lab Big Data & Intelligent Robot Guangzhou Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Pazhou Lab Guangzhou Peoples R China Kings Coll London Inst Psychiat Psychol & Neurosci Dept Biostat & Hlth Informat London England

In this article, we address the problem of difficulty-controllable visual question generation, which is to generate questions that satisfy the given difficulty levels based on the images and target answer. The existing approach seems to generate questions following templates. For easy questions, the model presents both answers and it becomes a choice question;while for hard questions, the answer set is not part of the question. In fact, question difficulty should be reflected by the objects and their relationships in the question. Towards this end, we propose a graph-based model with three concrete modules: Difficulty-controllable Graph Convolutional Network (DGCN) module, fusion module and difficulty-controllable decoder, to generate questions with a controllable level of difficulty. We first define a difficulty label based on the difficult index from the education area to represent the difficulty of a question. Next, a DGCN module is proposed to learn image representations that capture relations between objects in an image conditioned on a given difficulty label. Then, we use a fusion module to jointly attend the image representations and answer representations to capture answer-related image features. Finally, a difficulty-controllable decoder combines difficulty information into decoder initialization and input at each time step to control the difficulty of generated questions. Experimental results demonstrate that our framework not only achieves significant improvements on several automatic evaluation metrics, but also can generate difficulty-controllable questions.

关键词： Miltimodal Visual question generation Difficulty-controllable Graph convolutional network

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DRAKE: Deep Pair-Wise Relation Alignment for Knowledge-Enhanced Multimodal Scene Graph Generation in Social Media Posts

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2023年第7期33卷 3199-3213页

作者： Fu, Ze Zheng, Changmeng Feng, Junhao Cai, Yi Wei, Xiao-Yong Wang, Yaowei Li, Qing South China Univ Technol Key Lab Big Data & Intelligent Robot Minist Educ Guangzhou 510006 Peoples R China South China Univ Technol Sch Software Engn Guangzhou 510006 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Peng Cheng Lab Shenzhen 518066 Peoples R China Sichuan Univ Sch Comp Sci Chengdu 610017 Peoples R China

Scene Graph Generation (SGG) is a typical computer vision task that detects objects and corresponding predicates in an image. Existing SGG methods focus on modeling visual contexts to generate scene graphs and are conducted on well-annotated datasets with high-quality images. However, the quality is unguaranteed for images in social media posts, so that some images may be incomplete or occluded by some obstacles, hence might not provide sufficient visual context for SGG. Therefore, previous methods might result in missing or false visual relationship detection due to lacking visual contexts. To effectively generate the scene graphs in social media, we study multimodal scene graph generation (MSG) in this paper. MSG aims to develop visual scene graphs from images in social media posts with the support of text sentences. However, leveraging textual contents by simple multimodal alignment such as object-level alignment neglects the inherent pair-wise mapping between multimodal object pairs. To address the limitations, we propose a method named Deep pair-wise Relation Alignment for Knowledge-Enhanced (DRAKE) multimodal scene graph generation. The model supplements the missing visual contexts with well-aligned textual knowledge. It first represents the textual information into object-aware knowledge representation with the help of vision data. Furthermore, our proposed DRAKE facilitates the interaction of the info between multimodal pair-wise representations. A multimodal context enhancement layer can be devised to help the model generate the scene graph. To evaluate the model performance of SGG on social media images, we propose a social media SGG dataset called MSG. We comprehensively analyze the effectiveness of our proposed method on the MSG dataset. The experimental results on the MSG dataset indicate that our model outperforms the previous methods. To fairly compare our method with other SGG models, we also conduct experiments on the Visual Genome dataset for more

关键词： Scene graph generation social media posts knowledge enhancement pair-wise alignment

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TraSculptor: Visual Analytics for Enhanced Decision-Making in Road Traffic Planning

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IEEE Transactions on Visualization and Computer Graphics 2025年 PP卷 PP页

作者： Deng, Zikun Liu, Yuanbang Zhu, Mingrui Xiang, Da Yu, Haiyue Su, Zicheng Lu, Qinglong Schreck, Tobias Cai, Yi South China University of Technology School of Software Engineering China South China University of Technology Ministry of Education Key Laboratory of Big Data and Intelligent Robot China Tongji University Key Laboratory of Road and Traffic Engineering of the Ministry of Education China National University of Singapore Department of Civil and Environmental Engineering Singapore Graz University of Technology Austria

The design of urban road networks significantly influences traffic conditions, underscoring the importance of informed traffic planning. Traffic planning experts rely on specialized platforms to simulate traffic systems, assessing the efficacy of the road network across various states of modifications. Nevertheless, a prevailing issue persists: many existing traffic planning platforms exhibit inefficiencies in flexibly interacting with the road network's structure and attributes and intuitively comparing multiple states during the iterative planning process. This paper introduces TraSculptor, an interactive planning decision-making system. To develop TraSculptor, we identify and address two challenges: interactive modification of road networks and intuitive comparison of multiple network states. For the first challenge, we establish flexible interactions to enable experts to easily and directly modify the road network on the map. For the second challenge, we design a comparison view with a history tree of multiple states and a road-state matrix to facilitate intuitive comparison of road network states. To evaluate TraSculptor, we provided a usage scenario where the Braess's paradox was showcased, invited experts to perform a case study on the Sioux Falls network, and collected expert feedback through interviews. © 1995-2012 IEEE.

关键词： Visual analytics

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Balancing Constraints and Objectives by Considering Problem Types in Constrained Multiobjective Optimization

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第1期53卷 88-101页

作者： Xiang, Yi Yang, Xiaowei Huang, Han Wang, Jiahai South China Univ Technol Sch Software Engn Guangzhou 510006 Peoples R China South China Univ Technol Sch Software Engn Minist Educ Guangzhou 510006 Peoples R China South China Univ Technol Minist Educ Key Lab Big Data & Intelligent Robot Guangzhou 510006 Peoples R China Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510275 Peoples R China

Constrained multiobjective optimization problems widely exist in real-world applications. To handle them, the balance between constraints and objectives is crucial, but remains challenging due to non-negligible impacts of problem types. In our context, the problem types refer particularly to those determined by the relationship between the constrained Pareto-optimal front (PF) and the unconstrained PF. Unfortunately, there has been little awareness on how to achieve this balance when faced with different types of problems. In this article, we propose a new constraint handling technique (CHT) by taking into account potential problem types. Specifically, inspired by the prior work, problems are classified into three primary types: 1) I;2) II;and 3) III, with the constrained PF being made up of the entire, part and none of the unconstrained counterpart, respectively. Clearly, any problem must be one of the three types. For each possible type, there exists a tailored mechanism being used to handle the relationships between constraints and objectives (i.e., constraint priority, objective priority, or the switch between them). It is worth mentioning that exact problem types are not required because we just consider their possibilities in the new CHT. Conceptually, we show that the new CHT can make a tradeoff among different types of problems. This argument is confirmed by experimental studies performed on 38 benchmark problems, whose types are known, and a real-world problem (with unknown types) in search-based software engineering. Results demonstrate that within both decomposition-based and nondecomposition-based frameworks, the new CHT can indeed achieve a good tradeoff among different problem types, being better than several state-of-the-art CHTs.

关键词： Optimization Switches Statistics Sociology Software engineering Taxonomy Software algorithms Constrained multiobjective optimization constraint handling techniques (CHTs) problem types tradeoff model (ToM)

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Deep Reinforcement Learning-Based URLLC-Aware Task Offloading in Collaborative Vehicular Networks

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China Communications 2021年第7期18卷 134-146页

作者： Chao Pan Zhao Wang Zhenyu Zhou Xincheng Ren Hebei Key Laboratory of Power Internet of Things Technology North China Electric Power UniversityBeijing102206Baoding071003HebeiChina Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data Yanan UniversityYanan 716000China

Collaborative vehicular networks is a key enabler to meet the stringent ultra-reliable and lowlatency communications(URLLC)requirements.A user vehicle(UV)dynamically optimizes task offloading by exploiting its collaborations with edge servers and vehicular fog servers(VFSs).However,the optimization of task offloading in highly dynamic collaborative vehicular networks faces several challenges such as URLLC guaranteeing,incomplete information,and dimensionality *** this paper,we first characterize URLLC in terms of queuing delay bound violation and high-order statistics of excess ***,a Deep Reinforcement lEarning-based URLLCAware task offloading algorithM named DREAM is proposed to maximize the throughput of the UVs while satisfying the URLLC constraints in a besteffort *** with existing task offloading algorithms,DREAM achieves superior performance in throughput,queuing delay,and URLLC.

关键词： collaborative vehicular networks task of-floading URLLC awareness deep Q-learning

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Cost-Sensitive Portfolio Selection via Deep Reinforcement Learning (Extended Abstract)

Cost-Sensitive Portfolio Selection via Deep Reinforcement Le...

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International Conference on data Engineering

作者： Yifan Zhang Peilin Zhao Qingyao Wu Bin Li Junzhou Huang Mingkui Tan National University of Singapore Tencent AI Lab South China University of Technology Wuhan University University of Texas at Arlington Key Laboratory of Big Data and Intelligent Robot Ministry of Education Pazhou Laboratory

Portfolio Selection is an important real-world financial task and has attracted extensive attention in artificial intelligence communities. This task, however, has two main difficulties: (i) the non-stationary price series and complex asset correlations make the learning of feature representation very hard; (ii) the practicality principle in financial markets requires controlling both transaction and risk costs. Most existing methods adopt handcraft features and/or consider no constraints for the costs, which may make them perform unsatisfactorily and fail to control both costs in practice. In this paper, we propose a cost-sensitive portfolio selection method with deep reinforcement learning. Specifically, a novel two-stream portfolio policy network is devised to extract both price series patterns and asset correlations, while a new cost-sensitive reward function is developed to maximize the accumulated return and constrain both costs via reinforcement learning. We theoretically analyze the near-optimality of the proposed reward, which shows that the growth rate of the policy regarding this reward function can approach the theoretical optimum. We also empirically evaluate the proposed method on real-world datasets. Promising results demonstrate the effectiveness and superiority of the proposed method in terms of profitability, cost-sensitivity and representation abilities.

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Enhancing Paraphrase Question Generation With Prior Knowledge

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2023年 31卷 1464-1475页

作者： Xie, Jiayuan Fang, Wenhao Huang, Qingbao Cai, Yi Wang, Tao South China Univ Technol Sch Software Engn Guangzhou 510006 Peoples R China South China Univ Technol Key Lab Big Data & Intelligent Robot Minist Educ Guangzhou 510006 Peoples R China Pengcheng Lab Shenzhen 322099 Peoples R China Guangxi Univ Sch Elect Engn Nanning 530004 Peoples R China Kings Coll London Dept Biostat & Hlth Informat London 530004 England

Paraphrase question generation (PQG) aims to rewrite a given original question to a new paraphrase question, where the paraphrase question needs to have the same expressed meaning as the original question, but have a difference in expression form. Existing methods on PQG mainly focus on synonym substitution or word order adjustment based on the original question. However, rewriting based on the word-level may not guarantee the difference between paraphrase questions and original questions. In this paper, we propose a knowledge-aware paraphrase question generation model. Our model first employs a knowledge extractor to extract the prior knowledge related to the original question from the knowledge base. Then an attention mechanism and a gate mechanism are introduced in our model to selectively utilize the extracted prior knowledge for rewriting, which helps to expand the content of the generated question to maximize the difference. Additionally, we use a discriminator module to promote the generated paraphrase to be semantically close to the original question and the ground truth. Specifically, the loss function of the discriminator penalizes the excessive distance between the representation of the paraphrase question and the ground truth. Extensive experiments on the Quora dataset show that the proposed model outperforms the baselines. Further, our model is applied to the SQuAD dataset, which proves the generalization ability of our model in the existing QA dataset.

关键词： Task analysis Decoding Speech processing Logic gates Knowledge based systems Deep learning Internet Paraphrase question generation prior knowledge discriminator module

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