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A New Sequential Image Prediction Method Based on LSTM and DCGAN

computers, Materials & Continua 2020年第7期64卷 217-231页

作者： Wei Fang Feihong Zhang Yewen Ding Jack Sheng School of Computer&Software Jiangsu Engineering Center of Network MonitoringNanjing University of Information Science&TechnologyNanjing210044China State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing210023China Department of Economics FinanceInsurance and Risk Management University of Central ArkansasConway72035USA

Image recognition technology is an important field of artificial *** with the development of machine learning technology in recent years,it has great researches value and commercial *** a matter of fact,a single recognition function can no longer meet people’s needs,and accurate image prediction is the trend that people *** paper is based on Long Short-Term Memory(LSTM)and Deep Convolution Generative Adversarial Networks(DCGAN),studies and implements a prediction model by using radar image *** adopt a stack cascading strategy in designing network connection which can control of parameter convergence *** new method enables effective learning of image features and makes predictive models to have greater generalization *** demonstrate that our network model is more robust and efficient in terms of timing prediction than 3DCNN and traditional *** sequential image prediction model architecture proposed in this paper is theoretically applicable to all sequential images.

关键词： Image prediction LSTM DCGAN

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Distributionally Robust Optimization for Computation Offloading in Aerial Access Networks

arXiv

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arXiv 2024年

作者： Jiang, Guanwang Jia, Ziye He, Lijun Dong, Chao Wu, Qihui Han, Zhu Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing211106 China National Mobile Communications Research Laboratory Southeast University Jiangsu Nanjing211111 China School of Software Northwestern Polytechnical University Shaanxi Xi’an710072 China Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States

With the rapid increment of multiple users for data offloading and computation, it is challenging to guarantee the quality of service (QoS) in remote areas. To deal with the challenge, it is promising to combine aerial access networks (AANs) with multi-access edge computing (MEC) equipments to provide computation services with high QoS. However, as for uncertain data sizes of tasks, it is intractable to optimize the offloading decisions and the aerial resources. Hence, in this paper, we consider the AAN to provide MEC services for uncertain tasks. Specifically, we construct the uncertainty sets based on historical data to characterize the possible probability distribution of the uncertain tasks. Then, based on the constructed uncertainty sets, we formulate a distributionally robust optimization problem to minimize the system delay. Next, we relax the problem and reformulate it into a linear programming problem. Accordingly, we design a MEC-based distributionally robust latency optimization algorithm. Finally, simulation results reveal that the proposed algorithm achieves a superior balance between reducing system latency and minimizing energy consumption, as compared to other benchmark mechanisms in the existing literature. © 2024, CC BY.

关键词： Computation offloading

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Robust Multi-View Prototype Learning

Robust Multi-View Prototype Learning

引用

2020 International Conference on Internet of Things and Intelligent Applications, ITIA 2020

作者： Tian, Qing Xia, Shiyu Cao, Meng Cheng, Keyang Nanjing University of Information Science and Technology School of Computer and Software Nanjing China Southeast University School of Computer Science and Engineering Nanjing China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing China Jiangsu University School of Computer Science and Communication Engineering Zhenjiang China

ISBN: (纸本)9781728193014

In vision and machine learning, information fusion from multiple sensors can be regarded as multi-view learning paradigm to make use of the pairwise complementary information. Due to disturbed variances by illumination, equipment and environment, the collected data is frequently smeared by noises. Although there have been outlier-against works proposed, most of them suffer from redundant parameters. Even worse, few of them have modeled the cross-view complementary information. Actually, in multi-view learning, not only the clean data points, but also those outliers share the same prototypes in common space, by contrast, the margin value of the latter is non-zero. Motivated by this, in this article, we construct a robust multi-view prototype (RMVP) learning model with auxiliary margin matrices. Specifically, in RMVP, the projection matrices corresponding to each view span the common space to characterize the prototypes, simultaneously. In addition, an auxiliary sparse margin matrix for corresponding view identifies the distance gaps of outliers, whose non-zero column vector represents the margin value. To improve the robustness, the lasso regularization is penalized on the auxiliary matrices. And, an alternating optimization algorithm is presented to solve the proposed model. Finally, extensive experiments are constructed to testify the effectiveness of the proposed method. © 2020 IEEE.

关键词： Matrix algebra

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A Survey on Extreme Multi-label Learning

arXiv

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arXiv 2022年

作者： Wei, Tong Mao, Zhen Shi, Jiang-Xin Li, Yu-Feng Zhang, Min-Ling The School of Computer Science and Engineering Southeast University Nanjing210096 China The Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China The National Key Laboratory for Novel Software Technology Nanjing University Jiangsu Nanjing210023 China

Multi-label learning has attracted significant attention from both academic and industry field in recent decades. Although existing multi-label learning algorithms achieved good performance in various tasks, they implicitly assume the size of target label space is not huge, which can be restrictive for real-world scenarios. Moreover, it is infeasible to directly adapt them to extremely large label space because of the compute and memory overhead. Therefore, eXtreme Multi-label Learning (XML) is becoming an important task and many effective approaches are proposed. To fully understand XML, we conduct a survey study in this paper. We first clarify a formal definition for XML from the perspective of supervised learning. Then, based on different model architectures and challenges of the problem, we provide a thorough discussion of the advantages and disadvantages of each category of methods. For the benefit of conducting empirical studies, we collect abundant resources regarding XML, including code implementations, and useful tools. Lastly, we propose possible research directions in XML, such as new evaluation metrics, the tail label problem, and weakly supervised XML. © 2022, CC BY.

关键词： XML

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Driving Behavior Safety Assessment in Edge Computing Using Multitask Discriminative TS Fuzzy Model

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IEEE Transactions on Consumer Electronics 2024年

作者： Gu, Yi Chen, Zhiyu Wang, Lu Shen, Jinsong Bashir, Ali Kashif Wang, Wei Jiangnan University School of Artificial Intelligence and Computer Science Wuxi214122 China Jiangnan University Engineering Research Center of Intelligent Technology for Healthcare Ministry of Education Wuxi214122 China Chongqing University School of Big Data and Software Engineering Chongqing400044 China Jiangsu Keli Internet Information Technology Co. Ltd Jingjiang214500 China Manchester Metropolitan University Department of Computing and Mathematics United Kingdom Woxsen University Woxsen School of Business Hyderabad502 345 India Lebanese American University Department of Computer Science and Mathematics Beirut Lebanon Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China

Driving behavior safety analysis in edge computing involves the real-time collection and processing of vehicle driving data. Fuzzy models have advantages in analyzing driving behavior due to their excellent ability to handle uncertain information. This study develops a multitask discriminative Takagi-Sugeno fuzzy model (MD-TS-FM) for driving behavior safety assessment. To comprehensively analyze the correlation and differences in multitask driving behaviors, the designed consequent part consists of two parts: task-shared part reflects the shared intrinsic structure information that are consistent across different driving tasks, and task-specific part reflects distinct characteristics and variations specific to each task, allowing the model to address the unique aspects of different driving behaviors. Accordingly, the task-shared consequent part is characterized by low-rank property, which mines global structural information within the fuzzy space;whereas the task-specific consequent part exhibits sparsity, which removes non-discriminative and irrelevant information. This sparsity ensures that the model focuses on the most critical features for each specific task. Furthermore, a discriminative diversity term is introduced to enhance the diversity between tasks, which explores the consistent information of task-shared consequents while reducing the overlap of task-specific consequents. Experimental results indicate that the MD-TS-FM model can be effectively applied to driving behavior safety assessment. © 1975-2011 IEEE.

关键词： driving behavior safety assessment edge computing fuzzy model multitask learning

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Weak Fault Diagnosis of Rolling Bearing Based on Improved Stochastic Resonance

引用

computers, Materials & Continua 2020年第7期64卷 571-587页

作者： Xiaoping Zhao Yifei Wang Yonghong Zhang Jiaxin Wu Yunging Shi School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China School of Automation Nanjing University of Information Science and TechnologyNanjing210044China Electrical and Computer Engineering New Jersey Institute of TechnologyNewark07102USA Network Monitoring Center of Jiangsu Province Nanjing University of Information Science and TechnologyNanjing210044China

Stochastic resonance can use noise to enhance weak signals,effectively reducing the effect of noise signals on feature *** order to improve the early fault recognition rate of rolling bearings,and to overcome the shortcomings of lack of interaction in the selection of SR(Stochastic Resonance)method parameters and the lack of validation of the extracted features,an adaptive genetic random resonance early fault diagnosis method for rolling bearings was *** with the existing methods,the AGSR(Adaptive Genetic Stochastic Resonance)method uses genetic algorithms to optimize the system parameters,and further optimizes the parameters while considering the interaction between the *** method can effectively extract the weak fault features of the *** order to verify the effect of feature extraction,the feature signal extracted by AGSR method was input into the Fully connected neural network for fault *** practicality of the algorithm is verified by simulation data and rolling bearing experimental *** results show that the proposed method can effectively detect the early weak features of rolling bearings,and the fault diagnosis effect is better than the existing methods.

关键词： Rolling bearing weak fault stochastic resonance genetic algorithm neural network

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FreezePipe: An Efficient Dynamic Pipeline Parallel Approach Based on Freezing Mechanism for Distributed DNN Training

FreezePipe: An Efficient Dynamic Pipeline Parallel Approach ...

引用

International Conference on computer Supported Cooperative Work in Design

作者： Caishan Weng Zhiyang Shu Zhengjia Xu Jinghui Zhang Junzhou Luo Fang Dong Peng Wang Zhengang Wang School of Computer Science and Engineering Southeast University Nanjing P.R. China Boston College Nanjing P.R. China Data Science Center of Excellence Deloitte Consulting(Shanghai) Co. Ltd China Jiangsu HHigh-Tech Software Technology Co.Ltd

Deep Neural Network (DNN) training on a large scale is extremely time-consuming and computationally intensive, which is accelerated by distributed training. In recent years, pipeline parallelism has been developed, which enables partitioning the model across several devices, e.g. GPU, and training efficiency is improved by dividing data batches into micro-batches, with each of them processed by a different stage of the model. Currently, parallel training assumes pipeline placement and partitioning are static, with parameters updating each iteration, without accounting for freezing. This results in computational resources not being fully utilized. In this paper, we propose FreezePipe, a novel method for optimizing deep learning training that combines the freezing mechanism with pipeline parallel training. In FreezePipe, a lightweight method for determining the freezing strategy based on gradient changes is employed. Considering that resources need to be released based on the frozen layer, a lightweight model partitioning algorithm was designed to determine the optimal strategy for pipeline partitioning. Experimental results show that FreezePipe can reduce the training time by 64.5% compared to Torchgpipe on CIFAR-10 dataset without compromising any model performance.

关键词：

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SGNet: Salient Geometric Network for Point Cloud Registration

SGNet: Salient Geometric Network for Point Cloud Registratio...

引用

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Qianliang Wu Yaqing Ding Lei Luo Haobo Jiang Shuo Gu Chuanwei Zhou Jin Xie Jian Yang PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Visual Recognition Group Faculty of Electrical Engineering Czech Technical University in Prague Prague Czech Republic State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Intelligence Science and Technology Nanjing University Suzhou China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Point Cloud Registration (PCR) is a critical and challenging task in computer vision and robotics. One of the primary difficulties in PCR is identifying salient and meaningful points that exhibit consistent semantic and geometric properties across different scans. Previous methods have encountered challenges with ambiguous matching due to the similarity among patch blocks throughout the entire point cloud and the lack of consideration for efficient global geometric consistency. To address these issues, we propose a new framework that includes several novel techniques. Firstly, we introduce a semantic-aware geometric encoder that combines object-level and patch-level semantic information. This encoder significantly improves registration recall by reducing ambiguity in patch-level superpoint matching. Additionally, we incorporate a prior knowledge approach that utilizes an intrinsic shape signature to identify salient points. This enables us to extract the most salient super points and meaningful dense points in the scene. Secondly, we introduce an innovative transformer that encodes High-Order (HO) geometric features. These features are crucial for identifying salient points within initial overlap regions while considering global high-order geometric consistency. We introduce an anchor node selection strategy to optimize this high-order transformer further. By encoding inter-frame triangle or polyhedron consistency features based on these anchor nodes, we can effectively learn high-order geometric features of salient super points. These high-order features are then propagated to dense points and utilized by a Sinkhorn matching module to identify critical correspondences for successful registration. The experiments conducted on the 3DMatch/3DLoMatch and KITTI datasets demonstrate the effectiveness of our method.

关键词： Point cloud compression computer vision Accuracy Shape Semantics Transformers Feature extraction Encoding Intelligent robots

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Machine Learning-Based Recruitment Strategies for Multicenter Blood Donors: Design and Application

SSRN

引用

SSRN 2024年

作者： Zhao, Zuoyan Ye, Xiaofan Jiao, Yudong Wang, Mingyuan Cai, Lina Tian, Jinyue Zhang, Zihan Li, Zhenggang Yan, Huimin Zhou, Chun Ma, Yuchi Tang, Yinguang Sun, Tianbao Diao, Renhua Liu, Dexing Fan, Enyong Li, Shaobo Xu, Shouguang Yan, Weibin Xue, Hui Liang, Wenbiao School of Computer Science and Engineering Southeast University Nanjing210096 China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications [Southeast University Ministry of Education China Jiangsu Province Blood Center Nanjing China Yangzhou Blood Station Yangzhou China Suzhou Red Cross Blood Station Suzhou China College of Software Engineering Southeast University Nanjing210096 China

Background: Short Message Service (SMS) recruitment is crucial for recruiting blood donors, but current methods are inefficient, requiring many messages to improve the success rate. We aimed to enhance SMS recruitment efficiency using machine learning models. Methods: A multicenter study was conducted with blood donation data and SMS recruitment data from the blood stations in Nanjing (Jan 1, 2017, to Nov 30, 2022), Suzhou (Jan 1, 2018, to Apr 30, 2023), and Yangzhou (Oct 1, 1998, to Aug 25, 2024), China. Donor features such as age, gender, job, blood type, and donation history were used. For each city, various machine learning models were trained and the two best models were selected to be applied in subsequent SMS recruitments. Findings: In Nanjing, the best models were multi-layer perceptron (recall: 0·72, 95% CI: 0·65-0·80) and random forest (recall: 0·70, 95% CI: 0·68-0·82). In Suzhou and Yangzhou, multi-layer perceptron (recall: Suzhou: 0·70, 95% CI: 0·62-0·81;Yangzhou: 0·68, 95% CI: 0·61-0·74) and random forest (recall: Suzhou: 0·77, 95% CI: 0·62-0·86;Yangzhou: 0·64, 95% CI: 0·58-0·70) are also the best models. In Nanjing, Suzhou, and Yangzhou, recruitment success increased by 408·28%, 25·19%, and 47·31%, while SMS messages were reduced by 18·71%, 121·52%, and 138·75%, respectively. Recruitment efficiency per SMS improved by 9·54% in Nanjing, 53·61% in Suzhou, and 38·98% in Yangzhou. Interpretation: Our results suggested that recall was the primary evaluation metric, along with accuracy, precision, and F1 score, and that machine learning can improve efficiency and reduce the cost of recruitment, which has a positive influence on ensuring the blood supply. Funding: The Social Development Science and Technology Project of jiangsu Province (No. BE2022811), the National Natural Science Foundation of China (Nos. 62476056, 62076062, and 62306070), and the Big Data Computing Center of Southeast University. Declaration of Interest: We declare no competing interests.

关键词： Decision trees

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Research on a traffic flow statistical algorithm based on YBOVDT and SAM2

引用

Scientific Reports 2025年第1期15卷 1-21页

作者： Wang, Yuanyuan Ding, Zheng Liu, Jiange Wu, Kexiao Dipu, Md Sharid Kayes Ma, Tingmei Ma, Yonghao Zhang, Haiyan School of Computer and Software Engineering Huaiyin Institute of Technology Jiangsu Huai’an China Huai’an Power Supply Branch State Grid Jiangsu Electric Power Co. Ltd Jiangsu Huai’an China

In the process of urbanization, traffic flow statistics are of great significance to traffic management. Existing traffic flow statistics solutions suffer from incomplete functionality and lack effective solutions for core issues. The closed-set object detection algorithms they employ can only perform detections based on fixed categories, which leads to limited recognition scope and weak model generalization ***, the tracking algorithms used are unstable and have low computational efficiency. To address these challenges, this paper proposes a traffic flow statistical method based on YBOVDT(YOLO-World and BOT-SORT-Open Vocabulary Detection and Tracking)and ***, in the method, this paper proposes a “Traffic Flow Data Processing and Analysis” module, aiming to optimize and supplement the five core functions required for traffic flow statistics tasks, thereby making the functions of the entire solution more *** addition, this paper combines the latest open set object detection and tracking algorithms to enhance the recognition ability and tracking stability of traffic objects. In this study, a custom dataset was used to train existing traffic flow statistics *** experimental results showed that the YOLO-World model achieved a precision of 76.99% and an mAP50 of 70.08%. A comparative analysis with YOLO-v3,YOLO-v5, YOLO-v6,and YOLO-v8 algorithms indicated that, while balancing spatial and temporal resource consumption and accuracy, the proposed algorithm offers higher recognition accuracy and environmental adaptability. The experimental results further validated that this method demonstrates significant improvements in handling traffic flow statistics tasks in complex traffic environments. © The Author(s) 2025.

关键词： Multimodal learning Traffic flow statistics Transportation engineering

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