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检索条件"机构=Key Laboratory of Knowledge Engineering with Big Data"

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Task Separation and knowledge Sharing for Class Incremental Learning

Task Separation and Knowledge Sharing for Class Incremental ...

引用

Intelligent Computing and Human-Computer Interaction (ICHCI), International Conference on

作者： Jiali Zhang Xiaoyan Qiao Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion Technology and Evaluation Shandong Engineering Research Center School of Computer Science and Technology Shandong Technology and Business University Yantai Shandong China Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion Technology and Evaluation Shandong Engineering Research Center School of Mathematics and Information Science Shandong Technology and Business University Yantai Shandong China

ISBN: (数字)9798350368284

ISBN: (纸本)9798350368291

Methods based on dynamically expanding architectures can effectively mitigate catastrophic forgetting in class incremental learning (CIL), but they often overlook information sharing and integration between subnetworks, leading to performance degradation and increased parameter count and memory consumption as the network expands. To address these complex issues, we introduce a new method called Task Separation and knowledge Sharing (TSKS), which transfers knowledge at both the logical and feature levels through Hybrid knowledge Distillation (HKD). In logical distillation, we combine Global knowledge Distillation (GKD) and Task knowledge Distillation (TKD) to establish connections between tasks. To prevent the model from favoring new classes, we design an Attention- Based Feature Fusion (ABFF) mechanism to capture important features. Additionally, we introduce a Soft-Margin Auxiliary Loss (SMAL) to help the model learn new concepts. Extensive experiments show that TSKS outperforms existing models on CIFAR100 and ImageNet100, regardless of whether memory samples are used. Notably, TSKS demonstrates superior performance in memory-free settings, indicating a reduced reliance on rehearsal memory.

关键词： knowledge engineering Human computer interaction Degradation Incremental learning Computational modeling Memory management Information sharing Network architecture Feature extraction knowledge transfer

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Evaluation of China's Digital Economy: A Case Study using Entropy and TOPSIS 11

Evaluation of China's Digital Economy: A Case Study using En...

引用

11th International Conference on Information Technology and Quantitative Management, ITQM 2024

作者： Liu, Junjie Wang, Boya Xue, Jiayu Qu, Yi Shi, Yong Sino-Danish College University of Chinese Academy of Sciences Beijing100049 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China Faculty of Science and Engineering University of Nottingham Ningbo China Zhejiang Ningbo315100 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States

Recently, digital economy in social science has become increasingly significant. A well-constructed evaluation structure could generally and promptly describe and reveal its overall social digitalization development. Building on existent researches, this paper improves the evaluation system, which comprises four first-level indicators and thirty-one second-level indicators. Notably, this paper innovatively incorporates higher education into digital innovation of the evaluation. The Entropy method is utilized to assign weights to various indicators in an objective way, with employing the TOPSIS approach to prioritize the development of digital economy in places based on the closeness to the ideal solution. By analysing related data related from 31 provinces in China between 2017 to 2021, this paper greatly finds: Firstly, the development of digital economy vary significantly across provinces, with most provinces at a relatively poor level, indicating considerable potential for overall growth;Secondly, Guangdong, Jiangsu, Zhejiang, and Shandong consistently ranked in the top four from 2017 to 2021, reflecting their specific development attributes and their emphasis on digital economy;Thirdly, significant disparities exist both between regions and among provinces. Consequently, this paper suggests establishing a comprehensive national digital economy development plan, formulating region-specific digital economy policies tailored to local conditions, and encouraging leading provinces to actively share experiences with other provinces. © 2024 The Authors.

关键词： Digital economy Evaluation Multi-source information TOPSIS

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Independent Pitch Controller based on Fuzzy Adaptive Tuning PI for Load Mitigation of Large Land Wind Turbine 2

Independent Pitch Controller based on Fuzzy Adaptive Tuning ...

引用

2023 2nd International Conference on Energy Internet and Power Systems, ICEIPS 2023

作者： Li, Shengshan Du, Ming Li, Yanggui He, Jun Ma, Fuliang Chen, Qingyun Department of Computer Science Qinghai University Qinghai Xining China Geospatial and Natural Resources Big Data Center Qinghai Xining China Key Laboratory of Geospatial Information Technology and Application Qinghai Xining China College of Chemical Engineering Qinghai University Qinghai Xining China Qinghai Telecom Qinghai Xining China

With the development of wind power generation units towards the mega-scale, the blade root and tower have large load and vibration problems during the operation of wind turbines. In order to extend the operational life of wind turbines, a PI independent pitch control (IPC) strategy with fuzzy adaptive tuning (FAT) is introduced based on the existing wind turbine model and control strategy to achieve optimal control of wind turbine loads. Simulations are carried out by the FAST software of the US Renewable Energy laboratory to compare the independent pitch and the cooperative pitch. The results show that IPC reduces the load on the blade roots and improves the quality of the electrical energy. © 2023 Published under licence by IOP Publishing Ltd.

关键词： Wind turbines

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Polarization Reconfigurable Magneto-Electric Dipole Antenna Array

Polarization Reconfigurable Magneto-Electric Dipole Antenna ...

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2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022

作者： Hu, Jun Wong, Hang Lin, Wei Hefei University of Technology School of Microelectronics Hefei China City University of Hong Kong State Key Laboratory of Terahertz and Millimeter Waves Department of Electrical Engineering Hong Kong Hong Kong Global Big Data Technologies Centre School of Electrical and Data Engineering University of Technology Sydney UltimoNSW2007 Australia

ISBN: (数字)9781665496582

ISBN: (纸本)9781665496582

This paper reports a simple design method for wideband polarization reconfigurable magneto-electric (ME) dipole antenna and array. By only adopting two biasing voltages, the polarization can be switched among four polarizations, e.g., two orthogonal linearly polarized (LP) and two orthogonal circularly polarized (CP). This quad-polarization diversity is realized by systematically integrating a wideband reconfigurable feeding network and a ME dipole with two orthogonal feeding ports. By controlling the states of the switchable feeding network, two orthogonal LP mode corresponding to the two ports can be excited with specific phase difference, thereby generating four different polarizations. Moreover, the reconfigurable feeding network can be extended to array application without increasing any p-i-n diode. For proof of the concept, a 1 × 4 array is designed, fabricated and tested. The measured results confirm the array's capability for four reconfigurable polarizations. Moreover, the array also exhibits exceptional advantages as wide bandwidth and a simple switching topology. © 2022 IEEE.

关键词： Polarization

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A Semantic Framework for Chinese Historical Events Based on Linked data and knowledge Graph 16th

A Semantic Framework for Chinese Historical Events Based on ...

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16th International Conference on Diversity, Divergence, Dialogue, iConference 2021

作者： Wang, Hao Li, Yueyan Deng, Sanhong School of Information Management Nanjing University Nanjing210023 China Jiangsu Key Laboratory of Data Engineering and Knowledge Service Nanjing210023 China

ISBN: (纸本)9783030712914

In the era of big data, data redundancy has become an obstacle to deep reading. The objective of linked data as a new data organization model is to transform data into structured data following unified standards. The lack of Chinese conceptual terms has seriously hindered the semantization and standardization of Chinese domain ontology. Taking Chinese historical events as an example, ontology technology is used in this paper to standardize the definition of concepts and semantic relations in domain knowledge. Moreover, concepts from text resources are extracted through a deep learning algorithm Bi-LSTM-CRFs and combined with an external knowledge base to realize the fusion of related data within various data sets. Ultimately, the knowledge ontology of historical events is displayed in the way of a knowledge graph to further explore the practical application. The results show that the accuracy of the terms extraction of historical events is about 80% indicating the good recognition performance and portability of the model. © 2021, Springer Nature Switzerland AG.

关键词： Linked data

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TreeVI: reparameterizable tree-structured variational inference for instance-level correlation capturing 24

TreeVI: reparameterizable tree-structured variational infere...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Junxi Xiao Qinliang Su School of Computer Science and Engineering Sun Yat-sen University Guangzhou China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China and Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

ISBN: (纸本)9798331314385

Mean-field variational inference (VI) is computationally scalable, but its highly-demanding independence requirement hinders it from being applied to wider scenarios. Although many VI methods that take correlation into account have been proposed, these methods generally are not scalable enough to capture the correlation among data instances, which often arises in applications involving graphs or explicit constraints among instances. In this paper, we developed the Tree-structured Variational Inference (TreeVI), which uses a tree to capture the correlation among latent variables in the posterior. We show that samples from the tree-structured posterior can be reparameterized efficiently and parallelly, making its training cost just 2 or 3 times that of VI under the mean-field assumption. To capture correlation with more complicated structure, the TreeVI is further extended to the multiple-tree case. Furthermore, we show that the underlying tree structure can be automatically learned from training data. With experiments on synthetic datasets, constrained clustering, user matching and link prediction, we demonstrate the efficacy of TreeVI in capturing instance-level correlation in posteriors and enhancing the performance of downstream applications.

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PG-YOLO: A Lightweight Vehicle Detection Method for Drones

PG-YOLO: A Lightweight Vehicle Detection Method for Drones

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Yang Liu Tengyu Fan Kun Mei Liming Zhou Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China Department of Clinical Laboratory Huaihe Hospital of Henan University Kaifeng China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

With the rapid development of drone technology, object detection technology is widely applied in the field of drone intelligent transportation. In response to the problems of low accuracy and high computational complexity in vehicle detection tasks on resource limited drones, this paper proposes a lightweight and efficient improved model PG-YOLO for drone vehicle detection based on the YOLOv7-tiny model. Firstly, replace the LeakyReLU activation function with the SiLU activation function to compensate for the lack of nonlinear expression in the LeakyReLU activation function, and improve the convergence speed and generalization of the model during training; Secondly, the feature extraction and fusion modules were optimized to enhance the network’s perception and capture capabilities of the target, improve detection accuracy, and reduce the number of parameters; Then, lightweight convolution PConv and GSConv are introduced, and a lightweight module PPC based on PConv is proposed to improve the network’s Neck layer, reducing the number of parameters and computation while maintain detection performance; Finally, the CBAM attention mechanism is introduced to improve the detection performance for the problem of dense vehicle targets. The improved network achieved an average detection accuracy of 60.1%, an increase of 12.1 percentage points compared to the original network, a reduction of 73.6% in parameter count, a reduction of 15.9% in computational complexity, and a detection speed of 154 FPS. Compared to the original network, the network is more lightweight and achieves higher detection accuracy, which can more effectively perform unmanned aerial vehicle detection tasks.

关键词： Training Accuracy Attention mechanisms Vehicle detection Computational modeling Transportation Feature extraction Autonomous aerial vehicles Computational complexity Drones

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Grasp Control Method for Robotic Manipulator Based on Federated Reinforcement Learning

Grasp Control Method for Robotic Manipulator Based on Federa...

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Advanced Algorithms and Control engineering (ICAACE), International Conference on

作者： Yue Wang Shida Zhong Tao Yuan State Key Laboratory of Radio Frequency Heterogeneous Integration College of Electronics and Information Engineering Shenzhen University Shenzhen China Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication Shenzhen University Shenzhen China

ISBN: (数字)9798350361445

ISBN: (纸本)9798350361452

With development of mobile edge computing (MEC) and the Artificial Intelligent Internet of Things (AIoT), how to use intelligent devices to better apply to industrial scenarios has become a critical factor in promoting industry to reduce costs, increase efficiency, and enable development. In this paper, we analysis the problem of robotic manipulators working in dynamic environments and propose a model based on federated reinforcement learning (FRL) to control robotic manipulator grasping. In the proposed FRL framework, the optimal control policy is learned independently by multiple agents on their respective AIoT devices. Also, we use federated learning to ensure the safety of data and improve the reward function to accelerate the learning of model. We evaluate the performance of the proposed method and compare it with the traditional method based on RL. The experimental results show that proposed algorithm significantly improves the model training convergence speed by about 64% and the task average reward performance index by about 36% compared with baseline.

关键词： Training Service robots Heuristic algorithms Simulation Grasping Reinforcement learning data models

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LSU-NET: Lightweight Automatic Organs Segmentation Network for Medical Images

LSU-NET: Lightweight Automatic Organs Segmentation Network f...

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

作者： Yujie Ding Shenghua Teng Zuoyong Li Xiao Chen College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Minjiang University Fuzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

UNet and its variants have widespread applications in medical image segmentation. However, the substantial number of parameters and computational complexity of these models make them less suitable for use in clinical settings with limited computational resources. To address this limitation, we propose a novel Lightweight Shift U-Net (LSU-Net). We integrate the Light Conv Block and the Tokenized Shift Block in a lightweight manner, combining them with a dynamic weight multi-loss design for efficient dynamic weight allocation. The Light Conv Block effectively captures features with a low parameter count by combining standard convolutions with depthwise separable convolutions. The Tokenized Shift Block optimizes feature representation by shifting and capturing deep features through a combination of the Spatial Shift Block and depthwise separable convolutions. Dynamic adjustment of the loss weights at each layer approaches the optimal solution and enhances training stability. We validated LSU-Net on the UWMGI and MSD Colon datasets, and experimental results demonstrate that LSU-Net outperforms most state-of-the-art segmentation architectures.

关键词： Training Image segmentation Convolution Dynamic scheduling Stability analysis Resource management Speech processing Standards Monitoring Biomedical imaging

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An Efficient Reference-Point Based k Neighbors Algorithm for Imbalanced data 7

An Efficient Reference-Point Based k Neighbors Algorithm for...

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7th International Conference on Cloud Computing and big data Analytics, ICCCBDA 2022

作者： Wang, Junkuan Wu, Yanmin Qi, Jinli Chen, Zizhong Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing China ChongQing College of Electronic Engineering Department of Artificial Intelligence and Big Data Chongqing China ChongQing College of Electronic Engineering Department of General Education and International Studies Chongqing China

ISBN: (纸本)9781665487115

The kNNs are very simple, effective and online mul-ticategory learning algorithms, which makes them widely applica-ble to various fields. However, almost all kNNs have the problem of low accuracy in imbalanced data. In this paper, an interesting point is observed that the nearest neighbors might not always be the best samples for prediction, and a reference-point based k Neighbors algorithm (RPkN) is proposed. The proposed algorithm uses some reference-points to measure the distance between two data points rather than directly computing the distance. This makes it able to find better neighbors than their nearest ones, and the problem of low accuracy in imbalanced data sets could be solved. Therefore, the proposed method can achieve higher aver-age accuracy than the existing exact kNNs. In addition, it avoids the direct computation of distances between each pair of data points without using any tree structure, and this makes the RPkN decrease the time complexity to O(nlogn). The time complexity to classify a new data point is O(logn). The time complexity is lower than almost all the existing exact kNNs. The low time complexity and not relying on tree structures makes it very suitable to be paralleled for large-scale data processing when compared with other fast exact kNNs using tree structure. © 2022 IEEE.

关键词： Training Cloud computing big data Prediction algorithms data processing Mathematical models Classification algorithms

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