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2021 4th International Conference on Computer Information Science and Application Technology, CISAT 2021

作者： Zhang, Sihan Liu, Yaping Wang, Xile Zhang, Miaohui School of Artificial Intelligence Henan University Henan Kaifeng475004 China School of Mathematics and Statistics Henan University Henan Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Henan Kaifeng475004 China

Target detection in aerial images by high altitude unmanned aerial vehicles is one of the hot research topics. A detection that is efficient and accurate is of a high value in the military and civilian fields. However, due to the irregular distribution of targets and their various scales shown in aerial images, it is difficult for existing backbone networks to effectively extract target features based on deep learning. To address these problems, in the paper, an efficient backbone network called AerNet is proposed, to which a local feature enhancement module (LFEM) is added to fully extract discriminative features of aerial targets via multi-scale convolutional layers. The network AerNet consists of 55 layers, which can keep high spatial resolution in deeper layers and maintain a large receptive field. Experimental results show that our AerNet has achieved a satisfying detection performance on the DOTA benchmark. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 Licence.

关键词： Benchmarking

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Multi-modal Multi-emotion Emotional Support Conversation 9th

Multi-modal Multi-emotion Emotional Support Conversation

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19th International Conference on Advanced data Mining and Applications, ADMA 2023

作者： Liu, Guangya Dong, Xiao Wang, Meng-xiang Yu, Jianxing Gan, Mengjiao Liu, Wei Yin, Jian School of Artificial Intelligence Sun Yat-sen University Zhuhai China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Pazhou Lab Guangzhou510330 China Key Laboratory of Sustainable Tourism Smart Assessment Technology Ministry of Culture and Tourism Zhuhai China China National Institute of Standardization Beijing100088 China

ISBN: (纸本)9783031466601

This paper proposes a new task of Multi-modal Multi-emotion Emotional Support Conversation (MMESC), which has great value in various applications, such as counseling, daily chatting, and elderly company. This task aims to fully perceive the users’ emotional states from multiple modalities and generate appropriate responses to provide comfort for improving their feelings. Traditional works mainly focus on textual conversation, while a single-modal cannot accurately reflect the users’ emotions, such as saying fine with an inconsistent disgusting feeling. To address this problem, we propose a new task on multi-modalities and exploit a new method called FEAT for this new task. FEAT can integrate fine-grained emotional knowledge from multiple modalities. It first recognizes the users’ mental states based on an emotion-aware transformer. It then generates supportive responses using a hybrid method with multiple comfort strategies. To evaluate our method, we construct a large-scale dataset named MMESConv. It is almost two times larger than existing single-modal datasets. There are three modalities in this dataset (text, audio, and video) with fine-grained emotion annotations and strategy labels. Extensive experiments on this dataset demonstrate the advantages of our proposed framework. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Behavioral research

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EFFECTS OF IONOSPHERE ON LOWER-FREQUENCY SPACEBORNE SAR IMAGING

EFFECTS OF IONOSPHERE ON LOWER-FREQUENCY SPACEBORNE SAR IMAG...

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2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021

作者： Wang, Kuan Chen, Bingxu Li, Ning Guo, Zhengwei Fu, Zewen College of Computer and Information Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng China

The propagation of spaceborne synthetic aperture radar (SAR) signal is affected by the ionosphere, which will lead to the degradation of the imaging quality, especially in the conditions of low-frequency and wide-bandwidth. This paper analyzed the influence of ionosphere on SAR imaging under different carrier frequency, different bandwidth, different ionosphere total electron content (TEC), different azimuth resolution and different scintillation strength by time-domain simulation. The results show that the ionospheric dispersion effect leads to SAR image defocusing, which is closely related to the radar carrier frequency, signal bandwidth, azimuth resolution and TEC value on the propagation path. At the same time, due to the influence of time delay, the dispersion effect also leads to the range displacement of SAR image. The ionospheric scintillation effect reduces the azimuth resolution significantly and in extreme cases imaging is not possible. © 2021 IEEE

关键词： Synthetic aperture radar

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Out-of-Distribution Fault Diagnosis of Industrial Cyber-Physical Systems Based on Orthogonal Anchor Clustering With Adaptive Balance

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 48-60页

作者： Liu, Ruonan Hu, Puyuan Zhao, Siheng Sun, Zhijian Han, Te Pang, Zhibo Zhang, Weidong Shanghai Jiao Tong University Department of Automation Shanghai200240 China National Key Laboratory of Marine Engine Science and Technology Shanghai201108 China Crossocean of Suzhou Technology Suzhou215121 China Beijing Institute of Technology Center for Energy and Environmental Policy Research School of Management Beijing100081 China ABB Corporate Research Sweden Department of Automation Technology Vasteras722 26 Sweden KTH Royal Institute of Technology Department of Intelligent Systems Stockholm114 28 Sweden Henan University Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Kaifeng475001 China Shanghai Jiaotong University Department of Automation Shanghai200240 China

Given the critical role of rotating machinery in industrial cyber-physical systems (ICPS), ensuring their reliable operation is essential for the stability and safety of ICPS. Deep neural networks have demonstrated competitive results for intelligent fault diagnosis, which are usually trained via the historical data of all fault modes. However, in real engineering, it is usually difficult to collect samples and exhaust all failures during the training stage. As a result, out-of-distribution fault diagnosis (OOD-FD) becomes a more realistic problem that requires the methods to not only accurately diagnose the known faults, but also effectively recognize unknown ones. Therefore, a novel orthogonal anchor clustering with focal attention (FA-OAC) is proposed in this paper for OOD-FD. Firstly, an orthogonal anchor clustering (OAC) algorithm is proposed to fix the class center of each fault mode orthogonally and distinguish the known and unknown faults at the class level. Then, because the identifiability of different fault modes changes a lot in OOD-FD, the focal attention mechanism is applied to dynamically adjust the attention to different fault modes according to the distance loss of OAC, thus addressing the identifiability imbalance problem. To verify the effectiveness of the proposed method, different OOD-FD tasks are designed based on two rotating machinery datasets. The experimental results and comparison with state-of-the-art fault diagnosis methods have demonstrated that the proposed method has improved the OOD-FD performance greatly and therefore provides an effective tool in real engineering. © 2023 IEEE.

关键词： Rotating machinery

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A Hybrid BiLSTM-ATT Model for Improved Accuracy Sentiment analysis

A Hybrid BiLSTM-ATT Model for Improved Accuracy Sentiment An...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Lanxue Dang Chunyu Wang Hongyu Han Yan-E Hou Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer Science and Information Engineering Henan University Kaifeng China

Sentiment analysis is an important and challenging task in the field of natural language processing. Researchers have used vocabulary-based methods and machine learning methods to conduct research on sentiment analysis tasks. In recent years, deep learning has achieved great success in the field of sentiment analysis. However, for complex textual data, using a single model is often insufficient. Inspired by deep learning models, in this study, we proposed a hybrid model using BiLSTM and attention mechanism, called BiLSTM-ATT model, to solve the problem of sentiment analysis. First, we adopted the GloVe method to train the initialized word embeddings. GloVe converts textual information into word vectors, which can calculate the distance between words. Next, we used a convolutional neural network that can extract local features, and a BiLSTM that can extract long-range semantic features of text with bidirectional extraction of long-term dependencies. Finally, the attention mechanism was used to improve the performance of the model by calculating the weight of the data. Experimental results demonstrate that our proposed hybrid BiLSTM-ATT model outperforms traditional deep learning methods in Accuracy, Recall, and Fl-Score. Our method was compared with deep learning methods on the IMDB movie review dataset.

关键词： Deep learning Analytical models Sentiment analysis Semantics Feature extraction Motion pictures data models

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Multi-UAVs cooperative task assignment and path planning scheme

Multi-UAVs cooperative task assignment and path planning sch...

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2021 International Conference on Computer Network Security and Software Engineering, CNSSE 2021

作者： Du, Xiaoyu Guo, Qicheng Li, Hui Zhang, Yanyu International Joint Research Laboratory for Cooperative Vehicular Networks of Henan Henan University Kaifeng Henan475000 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng Henan475000 China Henan University Minsheng College Kaifeng Henan475000 China

The task assignment and path planning problems of multiple unmanned aerial vehicles (multi-UAVs) cooperative assistance roadside units (RSUs) for data collection are optimization problems with the goal of minimizing time and energy consumption. This paper proposes a hierarchical optimization scheme for multi-UAVs collaborative assistance RSUs data collection. This solution solves the problems that the number of UAVs needs to be set in advance, the convergence speed is slow when the number of tasks increases, and it is easy to fall into a local optimal solution, and the convergence accuracy is poor. First, the solution uses the K-means algorithm to allocate tasks and group RSUs to find the right number of UAVs to perform the task. Then, this paper proposes a hybrid optimization algorithm based on bionic learning for path planning. Finally, we set up a reasonable evaluation mechanism and conducted simulation experiments. The algorithm in this paper is compared with genetic algorithm, gray wolf algorithm and whale algorithm, the results show that the total cost of the task obtained by the proposed algorithm is the lowest, the algorithm stability is better, and the convergence accuracy is the highest. © Published under licence by IOP Publishing Ltd.

关键词： K-means clustering

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UniAdapter: All-in-One Control for Flexible Video Generation

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第6期35卷 6059-6073页

作者： Wang, Cong Hu, Panwen Zhao, Haoyu Guo, Yuanfan Gu, Jiaxi Dong, Xiao Han, Jianhua Xu, Hang Liang, Xiaodan Chinese University of Hong Kong Hong Kong School of Science and Engineering Shenzhen China Fudan University School of Computer Science and Technology Shanghai China Huawei Noah'ark Lab Shanghai201206 China Sun Yat-Sen University School of Artificial Intelligence Zhuhai Campus Zhuhai519082 China Shenzhen Campus of Sun Yat-sen University School of Intelligent Systems Engineering Guangdong No. 66 Gongchang Road Guangming District Shenzhen518107 China Peng Cheng Laboratory and Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China

Condition-based video generation aims to create video content based on given information that describes specific subjects. However, most existing works can only utilize a single condition to guide the denoising process, thereby limiting their applicability to specific scenarios. Although some works attempt to accommodate multiple conditions within one framework, they often require multiple encoders, leading to inefficiencies in integrating multi-condition features. In this work, we present a framework that, with the support of the proposed Unified Adapter (UniAdapter), enables simultaneous multi-condition control of video generation within a single model. To effectively merge these conditions, we propose a novel Probabilistic Multi-condition Concatenator (PMC) module, which employs a unified encoder to accommodate multiple conditions and concatenate condition features at the pixel level to achieve fine-grained control. Following the PMC module, we employ 2D down-sampling blocks to refine features for injection into the Video Diffusion Model (VDM). Moreover, our UniAdapter is designed to be model-agnostic and compatible with any U-Net-based VDM, offering a versatile solution for improving video generation quality. Experimental results on public benchmarks UCF-101 and MSR-VTT show that our method achieves superior results in both quantitative and qualitative evaluations. © 2025 IEEE.

关键词： Signal encoding

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Multi-level Relation Learning for Cross-domain Few-shot Hyperspectral Image Classification

arXiv

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arXiv 2023年

作者： Liu, Chun Yang, Longwei Li, Zheng Yang, Wei Han, Zhigang Guo, Jianzhong Yu, Junyong School of Computer and Information Engineering Henan University Henan Zhengzhou450046 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Henan Zhengzhou450046 China Henan Engineering Laboratory of Spatial Information Processing Henan University Henan Zhengzhou450046 China Henan Industrial Technology Academy of Spatio-Temporal Big Data Henan University Henan Zhengzhou450046 China College of Geography and Environmental Science Henan University Henan Zhengzhou450046 China Henan DeFan High-tech Software Co. Ltd Henan Zhengzhou450046 China

Cross-domain few-shot hyperspectral image classification focuses on learning prior knowledge from a large number of labeled samples from source domains and then transferring the knowledge to the tasks which contain few labeled samples in target domains. Following the metric-based manner, many current methods first extract the features of the query and support samples, and then directly predict the classes of query samples according to their distance to the support samples or prototypes. The relations between samples have not been fully explored and utilized. Different from current works, this paper proposes to learn sample relations on different levels and take them into the model learning process, to improve the cross-domain few-shot hyperspectral image classification. Building on current method of"Deep Cross-Domain Few-Shot Learning for Hyperspectral Image Classification" which adopts a domain discriminator to deal with domain-level distribution difference, the proposed method applies contrastive learning to learn the class-level sample relations to obtain more discriminable sample features. In addition, it adopts a transformer based cross-attention learning module to learn the set-level sample relations and acquire the attention from query samples to support samples. Our experimental results have demonstrated the contribution of the multi-level relation learning mechanism for few-shot hyperspectral image classification when compared with the state of the art methods. All the codes are available at github https://***/HENULWY/STBDIP. © 2023, CC BY.

关键词： Image enhancement

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A Deletable and Modifiable Blockchain Scheme Based on Record Verification Trees and the Multisignature Mechanism

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Computer Modeling in Engineering & Sciences 2021年第7期128卷 223-245页

作者： Daojun Han Jinyu Chen Lei Zhang Yatian Shen Yihua Gao Xueheng Wang Henan Key Laboratory of Big Data Analysis and Processing Henan UniversityKaifeng47500China Institute of Data and Knowledge Engineering Henan UniversityKaifeng47500China School of Computer and Information Engineering Henan UniversityKaifeng47500China Department of Network and Finance Agricultural Bank of China Limited Xuchang BranchXuchang461000China School of Software Pingdingshan UniversityPingdingshan467000China Department of Network and Finance Agricultural Bank of China Limited Nanyang BranchNanyang473000China

As one of the most valuable technologies,blockchains have received extensive attention from researchers and industry circles and are widely applied in various ***,data on a blockchain cannot be *** a result,it is impossible to clean invalid and sensitive data and correct erroneous ***,to a certain extent,hinders the application of blockchains in supply chains and Internet of *** address this problem,this study presents a deletable and modifiable blockchain scheme(DMBlockChain)based on record verification trees(RVTrees)and the multisignature scheme.(1)In this scheme,an RVTree structure is designed and added to the block *** RVTree can not only ensure that a record is true and valid but,owing to its unique binary structure,also verify whether modification and deletion requests are valid.(2)In DMBlockChain,the multisignature mechanism is also *** mechanism requires the stakeholders’signatures for each modification or deletion request and thus ensures that a record will not be modified arbitrarily.A user’s request is deemed valid only if it is dually verified by the RVTree and the multisignature *** analysis finds that DMBlockChain can provide a secure and valid means for modifying and deleting records in a block while ensuring the integrity of the block and that DMBlockChain can effectively save space in some scenarios that require frequent records modification.

关键词： Blockchain record verification trees multisignature DMBlockChain

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A Dynamic Acceleration Method for Remote Sensing Image processing Based on CUDA 12th

A Dynamic Acceleration Method for Remote Sensing Image Proce...

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12th EAI International Conference on Simulation Tools and Techniques, SIMUtools 2020

作者： Zuo, Xianyu Zhang, Zhe Qiao, Baojun Tian, Junfeng Zhou, Liming Zhang, Yunzhou Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Laboratory of Spatial Information Processing Henan University Kaifeng475004 China College of Computer and Information Engineering Henan University Kaifeng475004 China National Cultural Heritage Administration Beijing100010 China

ISBN: (纸本)9783030727918

The incredible increase in the volume of remote sensing data has made the concept of Remote Sensing as big data reality with recent technological developments. Remote sensing image processing is characterized with features of massive data processing and intensive computation, which makes the processes difficult. To optimize the remote sensing image processing for GPU, compute unified device architecture (CUDA) is widely used to implement remote sensing algorithms. However, the usage of GPU in remote sensing image processing has been constrained by the complexity of its implementation and configuration. Therefore, how to take fully advantage of the parallel organization of GPU architecture is awfully challenging. In this paper, a dynamic adaptive acceleration (DAA) method is proposed to determine calculation parameters of GPU adaptively and preprocess the input remote sensing images on host dynamically. By this method, we determine calculation parameters according to the hardware parameters of GPU firstly. And then, the input remote sensing images are reconstructed based on the calculation parameters. Finally, the preprocessed image blocks are arranged to stream tasks and executed on GPU respectively. Effectiveness of the proposed DAA method in accelerate remote sensing algorithm with point operations were verified by experiments in this paper, and the experimental results indicated that the DAA method can obtain better performance than traditional methods. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2021.

关键词： Graphics processing unit

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