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5th International Symposium on Signal Processing Systems, SSPS 2023

作者： Li, Qiqing Li, Ru Shen, Xin Lv, Wei School of Aliyun Big Data Applications Zhuhai College of Science and Technology China School of Aliyun Big Data Applications City University of Macau China

ISBN: (纸本)9798400700040

In the era of big data, there are more and more outdoor camera acquisition equipment. Due to the influence of extreme weather, such as fog, camera acquisition equipment is easy to lead to the decline of image quality and destroy the value of image application. Therefore, this paper will propose an advanced dehazing algorithm to make the foggy image clearer. Based on the principle of residual neural network, combined with attention mechanism and feature pyramid idea, this paper proposes an end to-end learning single image dehazing algorithm. Let the network learn the relationship between channels and pixels, and use the feature pyramid multi-scale fusion feature to restore the foggy image to a clear image. The SSIM score was 0.9687 and the PSNR score was 29.16. Very good results have been achieved on the RESIDE outdoor dataset. This paper finds the scores obtained by testing DCP, AOD-NET, DeHazeNet, and GFN methods on the same dataset. Compared with these four methods, there is a significant improvement. In particular, it is 15.39% higher than the DCP method on SSIM and 10.03% higher on PSNP. © 2023 ACM.

关键词： Statistical tests

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Automatic Brain Tumor Classification Based on Transfer Learning Models 3

Automatic Brain Tumor Classification Based on Transfer Learn...

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3rd International Symposium on Advances in Informatics, Electronics and Education, ISAIEE 2022

作者： Zhu, Jinhong Central South University Department of Data Science and Big Data Technology Changsha China

ISBN: (纸本)9781665463577

It is time-consuming and error-prone to manually determine whether there is a brain tumor in an image. However, traditional automatic classification algorithms have certain limitations, which makes the automation of brain tumor classification still a challenging problem. In this article, a new method for automatic classification of brain tumors is proposed, which combines neural network models with transfer learning methods, so as to improve or solve the problem of slow iteration and long time-consuming model generation, improve accuracy, and reduce parameter. In short, the convolutional neural network model (CNN) is combined with the method of transfer learning to achieve automatic image classification on the Brain Tumor Detection 2020 dataset provided by Model Whale. More specifically, during the experiment, Tensorflow was selected as the deep learning framework. First, the transfer learning method was used, and imagenet weights were used. Then, Comparing model performance by changing the choice of the backbone network of the CNN. Select the accuracy rate as the evaluation index, compare the performance of the model, use binary_crossentropy as the loss function, and the optimizer uses adam. In this paper, three backbone networks, VGG, MobileNet and ResNet, are compared. Experimental results indicate that the automatic classification of brain tumors with the combination of CNN model and transfer learning method has better performance and the VGG model has the best performance. © 2022 IEEE.

关键词： Neural network models

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Ultrasonic testing signal image processing based on improved set empirical mode decomposition 7

Ultrasonic testing signal image processing based on improved...

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7th International Conference on Information and Computer Technologies, ICICT 2024

作者： Zhao, Siqi Peng, Yuying Dalian Neusoft University Of Information Department Of Software And Big Data Technology Dalian City China

ISBN: (纸本)9798350385625

In view of the fact that the ensemble empirical modal decomposition (EEMD) method can avoid the modal aliasing phenomenon of the EMD method, it is still lacking in noise removal and retention of effective feature signals, so an ultrasonic nondestructive testing signal processing method combining EEMD and wavelet packets is proposed. In this method, the original signal is first processed by EEMD to ensure that the modal aliasing phenomenon is eliminated. However, this will cause the loss of some characteristic signals, and the lost signals will be retrieved through wavelet packet processing to improve the signal-to-noise ratio. In the process of decomposition signal reconstruction, the correlation coefficient method is used for feature selection, and the effective defect signal with high correlation coefficient is selected for reconstruction, which can not only effectively suppress modal aliasing, but also retain more feature signals. Through the experimental verification of the actual signal, the proposed improved method obtains a better signal-to-noise ratio, a lower root mean square error and a higher correlation coefficient, which is more suitable for processing ultrasonic nondestructive testing signals. © 2024 IEEE.

关键词： Defects

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Superior stability of Li5Mg@Cu anodes for lithium metal batteries:Investigating the suppression effects of magnesium on lithium dendrite growth

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材料科学技术（英文版） 2025年第8期211卷 288-302页

作者： Ruijun Yao Zhuoyu Li Longke Bao Rui Deng Kai Zheng Yiming Hu Jiahui Li Hao Zhang Shaobo Tu Rongpei Shi Junwei Wu Changming Li Xingjun Liu College of Materials Science and Engineering Institute of Materials Genome and Big DataHarbin Institute of Technology(Shenzhen)Shenzhen 518055China Department of Materials Process Engineering Graduate School of EngineeringNagoya UniversityFuro-choChikusa-kuNagoya464-8603Japan School of Physics and Materials Science Nanchang University999 Xuefu RoadHonggutan DistrictNanchang 330031China College of Materials Science and Engineering Institute of Materials Genome and Big DataHarbin Institute of Technology(Shenzhen)Shenzhen 518055China 21C Innovation Laboratory(21C LAB) Contemporary Amperex Technology Ltd.Ningde 352100China Institute for Materials Science and Devices Suzhou University of Science and TechnologySuzhou 215011China

Li metal is widely recognized as the desired anode for next-generation energy storage,Li metal batteries,due to its highest theoretical capacity and lowest ***,it suffers from unstable elec-trochemical behaviors like dendrite growth and side reactions in practical ***,we report a highly stable anode with collector,Li5Mg@Cu,realized by the melting-rolling *** Li5Mg@Cu anode delivers ultrahigh cycle stability for 2000 and 1000 h at the current densities of 1 and 2 mA cm-2,respectively in symmetric ***,the Li5Mg@Cu|LFP cell exhibits a high-capacity retention of 91.8％for 1000 cycles and 78.8％for 2000 cycles at 1 ***,we investigate the suppression effects of Mg on the dendrite growth by studying the performance of LixMg@Cu electrodes with different Mg contents(2.0-16.7 at％).The exchange current density,surface energy,Li+diffusion coefficient,and chem-ical stability of LixMg@Cu concretely reveal this improving suppression effect when Mg content becomes *** addition,a Mg-rich phase with"hollow brick"morphology forming in the high Mg content LixMg@Cu guides the uniform deposition of *** study reveals the suppression effects of Mg on Li dendrites growth and offers a perspective for finding the optimal component of Li-Mg alloys.

关键词： Lithium dendrite Lithium metal anode Lithium-magnesium alloy Cycle performance Suppression effect Stability

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ControlVideo: conditional control for one-shot text-driven video editing and beyond

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science China(Information sciences) 2025年第3期68卷 150-162页

作者： Min ZHAO Rongzhen WANG Fan BAO Chongxuan LI Jun ZHU Department of Computer Science and Technology Institute for AI Tsinghua-Bosch Joint ML CenterTsinghua Laboratory of Brain and Intelligence Lab Tsinghua University ShengShu Technology Gaoling School of Artificial Intelligence Renmin University of China Beijing Key Laboratory of Big Data Management and Analysis Methods Pazhou Laboratory (Huangpu)

This paper presents ControlVideo for text-driven video editing — generating a video that aligns with a given text while preserving the structure of the source video. Building on a pre-trained text-to-image diffusion model, ControlVideo enhances the fidelity and temporal consistency by incorporating additional conditions(such as edge maps), and fine-tuning the key-frame and temporal attention on the source video-text pair via an in-depth exploration of the design space. Extensive experimental results demonstrate that ControlVideo outperforms various competitive baselines by delivering videos that exhibit high fidelity w.r.t. the source content, and temporal consistency, all while aligning with the text. By incorporating low-rank adaptation layers into the model before training, ControlVideo is further empowered to generate videos that align seamlessly with reference images. More importantly, ControlVideo can be readily extended to the more challenging task of long video editing(e.g., with hundreds of frames), where maintaining long-range temporal consistency is crucial. To achieve this, we propose to construct a fused ControlVideo by applying basic ControlVideo to overlapping short video segments and key frame videos and then merging them by pre-defined weight functions. Empirical results validate its capability to create videos across 140 frames, which is approximately 5.83 to 17.5 times more than what previous studies achieved. The code is available at https://***/thu-ml/controlvideo.

关键词： diffusion models controllable generation text-driven editing video editing long video editing

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BGNN: Behavior-aware graph neural network for heterogeneous session-based recommendation

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Frontiers of Computer science 2023年第5期17卷 103-118页

作者： Jinwei LUO Mingkai HE Weike PAN Zhong MING College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China National Engineering Laboratory for Big Data System Computing Technology Shenzhen UniversityShenzhen 518060China

Session-based recommendation(SBR)and multibehavior recommendation(MBR)are both important problems and have attracted the attention of many researchers and *** from SBR that solely uses one single type of behavior sequences and MBR that neglects sequential dynamics,heterogeneous SBR(HSBR)that exploits different types of behavioral information(e.g.,examinations like clicks or browses,purchases,adds-to-carts and adds-to-favorites)in sequences is more consistent with real-world recommendation scenarios,but it is rarely *** efforts towards HSBR focus on distinguishing different types of behaviors or exploiting homogeneous behavior transitions in a sequence with the same type of ***,all the existing solutions for HSBR do not exploit the rich heterogeneous behavior transitions in an explicit way and thus may fail to capture the semantic relations between different types of ***,all the existing solutions for HSBR do not model the rich heterogeneous behavior transitions in the form of graphs and thus may fail to capture the semantic relations between different types of *** limitation hinders the development of HSBR and results in unsatisfactory *** a response,we propose a novel behavior-aware graph neural network(BGNN)for *** BGNN adopts a dual-channel learning strategy for differentiated modeling of two different types of behavior sequences in a ***,our BGNN integrates the information of both homogeneous behavior transitions and heterogeneous behavior transitions in a unified *** then conduct extensive empirical studies on three real-world datasets,and find that our BGNN outperforms the best baseline by 21.87%,18.49%,and 37.16%on average correspondingly.A series of further experiments and visualization studies demonstrate the rationality and effectiveness of our *** exploratory study on extending our BGNN to handle more than two types of behaviors show that our BGNN can e

关键词： session-based recommendation graph neural network heterogeneous behaviors

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Evaluating quality of motion for unsupervised video object segmentation

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Optoelectronics Letters 2024年第6期20卷 379-384页

作者： CHENG Guanjun SONG Huihui Jiangsu Key Laboratory of Big Data Analysis Technology Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment TechnologyNanjing University of Information Science&TechnologyNanjing 210044China12

Current mainstream unsupervised video object segmentation(UVOS) approaches typically incorporate optical flow as motion information to locate the primary objects in coherent video frames. However, they fuse appearance and motion information without evaluating the quality of the optical flow. When poor-quality optical flow is used for the interaction with the appearance information, it introduces significant noise and leads to a decline in overall performance. To alleviate this issue, we first employ a quality evaluation module(QEM) to evaluate the optical flow. Then, we select high-quality optical flow as motion cues to fuse with the appearance information, which can prevent poor-quality optical flow from diverting the network's attention. Moreover, we design an appearance-guided fusion module(AGFM) to better integrate appearance and motion information. Extensive experiments on several widely utilized datasets, including DAVIS-16, FBMS-59, and You Tube-Objects, demonstrate that the proposed method outperforms existing methods.

关键词： Evaluating quality of motion for unsupervised video object segmentation

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Transforming the cybersecurity space of healthcare IoT devices using Deep Learning 4

Transforming the cybersecurity space of healthcare IoT devic...

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4th IEEE International Conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Kumar, Priyan Malarvizhi Kavin, Balasubramanian Prabhu Jagathpally, Abhignya Shahwar, Tayyaba University of North Texas Department of Data Science Denton United States SRM Institute of Science and Technology Department of Data Science and Business Systems TamilNadu India

ISBN: (纸本)9798331518882

The proliferating growth of IoT devices has created a great revolution in the lives of mankind especially in the healthcare sector. On the other side, there is an inclining trend of cyberattacks on healthcare IoT devices as the clinical data is very sensitive and not many healthcare organizations impart stringent security protocols on Internet of Medical Things (MIoT) devices. The distinct property of IoT devices and their resource constrained nature limits the effectiveness of existing security solutions. To combat this issue, the present research work proposes a Deep Gated Recurrent Unit (D-GRU) as an AI based framework to predict the onset of attacks in MIoT devices in Critical Care Units (CCU). The proposed model explores the various network and communication parameters of the patient and environment monitoring devices employed in CCU to detect the attack. This framework is lightweighted and hence can be easily deployed in healthcare sectors, which would eventually transform the cybersecurity landscape of MIoT devices. The proposed model is used to predict the onset of cyberattacks in open source, synthetic IoT healthcare data generated through IoT Flock tool. The intricate performance analysis of the model on various classification metrics shows that the model is effect in detecting the attacks in MIoT. © 2025 IEEE.

关键词： Critical care units Cyber attacks Environment monitoring Gated Recurrent Units MIoT network traffic parameters Patient monitoring

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Peer-to-Peer International Trade Model Based on Blockchain and Smart Contracts: Theory and Practice of BTLC 2

Peer-to-Peer International Trade Model Based on Blockchain a...

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2nd International Conference on big data and Privacy Computing, BDPC 2024

作者： Yan, Junjie Wang, Wennan Feng, Duanyang Song, Shiyang Li, Chengyifan Application College Zhuhai College of Science and Technology Alibaba Cloud Big Data Zhuhai China Academy of Management Guangdong University of Science and Technology Dongguan China City University of Macau Faculty of Data Science China

ISBN: (纸本)9798350307597

This study focuses on BTLC, a peer-to-peer international trade model based on blockchain and smart contracts, which aims to reform the traditional let- ter of credit process, improve international trade efficiency, reduce costs and improve security. By elaborating the BTLC model, this study demonstrates how blockchain technology as well as smart contract technology can be applied in the field of international trade to effectively solve the problems existing in the tra- ditional letter of credit process, such as high transaction costs due to too many intermediaries, high transaction risks due to an imperfect trust mechanism, and inefficiency in transactions. In addition, this paper further proves the superiority of the BTLC model in theory and practice through empirical research. It provides an in-depth discussion of the potential application and development direction of the model in future research. However, there are still some challenges and lim- itations in the practical application of the BTLC model. These challenges and limitations include the need for further empirical studies to validate the model's effectiveness and the problems faced. These include technical standards, laws and regulations. Therefore, this study suggests that future research directions should focus on the implementation challenges of blockchain technology, as well as the application of blockchain technology in other fields, in order to promote the wide application of blockchain technology in international trade. © 2024 IEEE.

关键词： Smart contract

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STIOS: A Novel Self-supervised Diffusion Model for Trajectory Imputation in Open Environment Scenarios 17

STIOS: A Novel Self-supervised Diffusion Model for Trajector...

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IEEE Congress on Cybermatics: 17th IEEE International Conference on Internet of Things, iThings 2024, 20th IEEE International Conference on Green Computing and Communications, GreenCom 2024, 17th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE International Conference on Smart data, Smartdata 2024

作者： Hu, Zhijing Yan, Hao Zheng, Yuhao He, Hua Chen, Chao Fan, Changjun Huang, Kuihua National University of Defense Technology Laboratory for Big Data Data and Decision Changsha China Central South University School of Computer Science and Engineering Changsha China

ISBN: (纸本)9798350351637

With the growth of location-based services, the accumulation of large amounts of trajectory data comes with the challenge of missing data. Existing trajectory imputation methods rely on deterministic models that cannot take into account open environments scenarios without auxiliary information. This may lead to poor performance in learning the long-term dependencies of trajectories. In this paper, we propose a novel Self-supervised diffusion model for Trajectory Imputation in Open environment Scenarios (STIOS), which combines a deterministic model and a conditional diffusion model. In addition, we propose a self-supervised training method inspired by masked language modelling that divides the data into conditional information and estimation targets. Extensive experiments on two real datasets demonstrate the superiority of STIOS for trajectory imputation. © 2024 IEEE.

关键词： Self-supervised learning

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