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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1203-1216页

作者： Li, Changping Wang, Bingshu Zheng, Jiangbin Zhang, Yongjun Chen, C.L. Philip Northwestern Polytechnical University School of Software Xi’an710129 China Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen518060 China Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China South China University of Technology School of Computer Science and Engineering Guangzhou510641 China

Frequent road incidents cause significant physical harm and economic losses globally. The key to ensuring road safety lies in accurately perceiving surrounding road incidents. However, the highly dynamic nature of traffic introduces significant challenges, particularly in detecting sudden and temporary incidents. In this article, we propose a novel detection framework, multihead attention ResNet with dynamic bottleneck (DB_RESNET_MHA), to identify physical unsigned road incidents. Our approach introduces three key innovations. First, we develop a tailored data augmentation strategy to create images that closely mimic the complex variations found in real-world road environments. Second, we enhance model expressiveness by employing attention mechanisms to nonlinearly integrate convolutional kernels within the residual network. Furthermore, we refine the prediction head by applying spatially distinct attention weights, enabling the model to capture intricate correlations between different features more effectively. To demonstrate the effectiveness of our method, we create a dataset for unsigned road incidents (UnsignRI), comprising a total of 16 323 images that capture 12 distinct types of incidents. It stands out as the most comprehensive dataset in the field, encompassing a wide range of geographical features and incident categories. Experimental results show that DB_RESNET_MHA achieves an average accuracy of 96.2% and a f1-score of 0.955 across various categories of unsigned incidents, surpassing other models. © 2020 IEEE.

关键词： Highway accidents

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Low power consumption reduced state and transition MLSE in optical interconnects

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Science China(information Sciences) 2023年第10期66卷 325-326页

作者： Weihao NI Fan LI Wei WANG Zhibin LUO Xingwen YI Yi CAI Zhaohui LI School of Electronics and Information Technology Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Sun Yat-sen University Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) Jiangsu Engineering Research Center of Novel Optical Fiber Technology and Communication Network Suzhou Key Laboratory of Advanced Optical Communication Network Technology Soochow University

Intensity modulation/direct detection(IM/DD) optical fiber communication system is an appropriate candidate for optical interconnect applications due to its low cost and low power consumption characteristics [1]. 4-level pulse amplitude modulation(PAM-4) is widely discussed in optical interconnects due to its simpler transceiver configuration and implementation [2].

关键词： Optical interconnects

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A Range-Based Node Localization Scheme for UWASNs Considering Noises and Aided With Neurodynamics Model

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IEEE/CAA Journal of Automatica Sinica 2023年第11期10卷 2168-2170页

作者： Lijuan Wang Xiujuan Du Chong Li School of Computer Qinghai Normal UniversityQinghai Provincial Key Laboratory of IoTXining 810008 the State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining 810008 Academy of Plateau Science and Sustainability Xining 810008China

Dear Editor,This letter focuses on the node localization problem in underwater acoustic sensor networks(UWASNs) with the time-dependent property and various noise disturbances. A range-based localization scheme aided with an integral-feedback-based neurodynamics(IND)model is proposed and referred to as IND-RS, which has the stability against the internal perturbations encountered during the solving process.

关键词： dynamics underwater networks

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A New Method of Image Restoration technology Based on WGAN

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computer Systems Science & Engineering 2022年第5期41卷 689-698页

作者： Wei Fang Enming Gu Weinan Yi Weiqing Wang Victor S.Sheng School of Computer&Software Engineering Research Center of Digital ForensicsMininstry of EducationNanjing University of Information Science&TechnologyNanjing210044China Provincial Key Laboratory for Computer Information Processing Technology Soochow UniversitySuzhou215325China Texas Tech University USA

With the development of image restoration technology based on deep learning,more complex problems are being solved,especially in image semantic inpainting based on ***,image semantic inpainting techniques are becoming more ***,due to the limitations of memory,the instability of training,and the lack of sample diversity,the results of image restoration are still encountering difficult problems,such as repairing the content of glitches which cannot be well integrated with the original ***,we propose an image inpainting network based on Wasserstein generative adversarial network(WGAN)*** the corresponding technology having been adjusted and improved,we attempted to use the Adam algorithm to replace the traditional stochastic gradient descent,and another algorithm to optimize the training used in recent *** evaluated our algorithm on the ImageNet *** obtained high-quality restoration results,indicating that our algorithm improves the clarity and consistency of the image.

关键词： Image restoration WGAN DCGAN context semantic

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A Privacy Preservation Method for Attributed Social Network Based on Negative Representation of information

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computer Modeling in Engineering & Sciences 2024年第7期140卷 1045-1075页

作者： Hao Jiang Yuerong Liao Dongdong Zhao Wenjian Luo Xingyi Zhang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei230601China Chongqing Research Institute School of Computer Science and Artificial IntelligenceWuhan University of TechnologyWuhan430070China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and TechnologyHarbin Institute of TechnologyShenzhen518055China

Due to the presence of a large amount of personal sensitive information in social networks,privacy preservation issues in social networks have attracted the attention of many *** by the self-nonself discrimination paradigmin the biological immune system,the negative representation of information indicates features such as simplicity and efficiency,which is very suitable for preserving social network ***,we suggest a method to preserve the topology privacy and node attribute privacy of attribute social networks,called ***,a negative survey-based method is developed to disturb the relationship between nodes in the social network so that the topology structure can be kept ***,a negative database-based method is proposed to hide node attributes,so that the privacy of node attributes can be preserved while supporting the similarity estimation between different node attributes,which is crucial to the analysis of social *** evaluate the performance of the AttNetNRI,empirical studies have been conducted on various attribute social networks and compared with several state-of-the-art methods tailored to preserve the privacy of social *** experimental results show the superiority of the developed method in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topology disturbing and attribute hiding *** experimental results show the superiority of the developed methods in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topological interference and attribute-hiding components.

关键词： Attributed social network topology privacy node attribute privacy negative representation of information negative survey negative database

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Offline Reinforcement Learning Based on Next State Supervision

Offline Reinforcement Learning Based on Next State Supervisi...

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

作者： Jie Yan Quan Liu Lihua Zhang School of Computer Science and Technology Soochow University Soochow China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Soochow China

Offline reinforcement learning aims to maximize the use of static offline datasets to train agents without interacting with the environment. For the problem of distribution shifts, most approaches avoid out-of-distribution actions through strong constraints, and do not consider generalization and learning within these domains. Based on this problem, we propose a novel method offline reinforcement learning based on next state supervision (NSS), which consists of two main components, the guidance policy and an adaptive coefficient. The guidance policy outputs the next-state with the highest value within a certain range around the current state and an adaptive coefficient regulates the weight of the penalty term in the learned policy. Empirical studies show that the method improves the performance of the baseline method with constraints while having some generalization ability.

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Offline Reinforcement Learning with Policy Guidance and Uncertainty Estimation

Offline Reinforcement Learning with Policy Guidance and Unce...

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

作者： Lan Wu Quan Liu Lihua Zhang Zhigang Huang School of Computer Science and Technology Soochow University Soochow China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Soochow China

Offline reinforcement learning is an approach for transforming static datasets into powerful decision engines. It cannot interact with the environment online, which leads to distribution shifts. Previous approaches addressed this problem by making the current policy as close as possible to the behavior policy. However, this type of approach severely limits the generalization ability of Q-functions. To address the above concerns, offline reinforcement learning with policy guidance and uncertainty estimation (PGUE) is proposed. PGUE proposes a fine-grained adjustment approach that improves the generalization ability of Q-functions using a perturbation model. The enhancement of the out-of-distribution generalization of Q-functions is achieved through the implicit guidance of the state space via a deterministic latent policy. Meanwhile, integrating uncertainty estimation into the loss function improves the in-distribution generalization of Q-functions. On the D4RL benchmark, PGUE has better performance than baselines. Moreover, we verify the state distribution and its out-of-distribution generalization ability.

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Offline Reinforcement Learning with Generative Adversarial Networks and Uncertainty Estimation

Offline Reinforcement Learning with Generative Adversarial N...

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

In recent years, offline reinforcement learning has attracted considerable attention in artificial intelligence. By generating a static dataset through a behavior policy, it is unable to engage in online interactions with the environment. However, this inevitably leads to states or actions undergoing inherent distribution shifts. To address the above concerns, offline reinforcement learning with generative adversarial networks and uncertainty estimation (GANUE) is proposed. This approach parameterizes the conditional generative adversarial networks and avoids the over-constraint problem that would be introduced when using a distance metric. Estimating the Q-value through ensemble uncertainty not only relaxes the policy constraint strength but also enhances the out-of-distribution generalization ability. The experimental results show that GANUE significantly performs better on the Maze2D and Adroit tasks than multiple baselines. In addition, we perform sensitivity analysis experiments for the parameters of the perturbation model.

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Unsupervised Nonlinear Adaptive Manifold Learning for Global and Local information

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Tsinghua Science and technology 2021年第2期26卷 163-171页

作者： Jiajun Gao Fanzhang Li Bangjun Wang Helan Liang School of Computer Science and Technology Soochow UniversitySuzhou 215006China Joint International Research Laboratory of Machine Learning and Neuromorphic Computing Provincial Key Laboratory for Computer Information Processing TechnologySoochow UniversitySuzhou 215006China

In this paper,we propose an Unsupervised Nonlinear Adaptive Manifold Learning method(UNAML)that considers both global and local *** this approach,we apply unlabeled training samples to study nonlinear manifold features,while considering global pairwise distances and maintaining local topology *** method aims at minimizing global pairwise data distance errors as well as local structural *** order to enable our UNAML to be more efficient and to extract manifold features from the external source of new data,we add a feature approximate error that can be used to learn a linear ***,we add a feature approximate error that can be used to learn a linear *** addition,we use a method of adaptive neighbor selection to calculate local structural *** paper uses the kernel matrix method to optimize the original *** algorithm proves to be more effective when compared with the experimental results of other feature extraction methods on real face-data sets and object data sets.

关键词： unsupervised manifold learning global and local information,adaptive neighbor selection method kernel matrix

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GOAL: Generalized Jointly Sparse Linear Discriminant Regression for Feature Extraction

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 4959-4971页

作者： Lu, Haoquan Lai, Zhihui Zhang, Junhong Yu, Zhuozhen Wen, Jiajun Shenzhen University Computer Vision Institute College of Computer Science and Software Engineering Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen518060 China Peking University Shenzhen Graduate School School of Electronic and Computer Engineering Shenzhen518055 China

Ridge regression (RR)-based methods aim to obtain a low-dimensional subspace for feature extraction. However, the subspace's dimensionality does not exceed the number of data categories, hence compromising its capability of feature representation. Moreover, these methods with L2-norm metric and regularization cannot extract highly robust features from data with corruption. To address these problems, in this article, we propose generalized jointly sparse linear discriminant regression (GOAL), a novel regression method based on joint L2,1-norm and capped-L2-norm, which can integrate sparsity, locality, and discriminability into one model to learn a full-rank robust feature extractor. The sparsely selected discriminative features are robust enough to characterize the decision boundary between classes. Locality is related to manifold structure and Laplacian smoothing, which can enhance the robustness of the model. By using the multinorm metric and regularization regression framework, the proposed method obtains the projection with joint sparsity and guarantees that the rank of the projection matrix will not be limited by the number of classes. An iterative algorithm is proposed to compute the optimal solution. Complexity analysis and proofs of convergence are also given in the article. Experiments on well-known datasets demonstrate our model's superiority and generalization ability. © 2020 IEEE.

关键词： Data mining

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