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2023 3rd International Conference on Intelligent Traffic Systems and Smart City, ITSSC 2023

作者： Zhang, Zixiang Zhao, Zhilong Deng, Jiaming Zhao, Yingying Xu, Tao Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Key Laboratory of Geographical Information Science Ministry of Education East China Normal University Shanghai200241 China School of Computer and Information Engineering Henan University Kaifeng475004 China International Education College Henan University Zhengzhou450046 China Henan Technology Innovation Center of Spatio-Temporal Big Data Henan University Zhengzhou450046 China Henan Industrial Technology Academy of Spatio-Temporal Big Data Henan University Zhengzhou450046 China

ISBN: (纸本)9781510672963

Urban travel data typically encompass various temporal and spatial scales and features. Nonetheless, existing traffic travel feature analysis often focuses on a single spatial scale, which may overlook crucial information and hinder features at different scales. Consequently, this could reduce the accuracy of travel pattern recognition. To solve this problem, we introduce a multi-scale fusion transformation algorithm. In our approach, we first establish a multi-level taxi travel data grid system. Then, we apply the I-Clique clustering algorithm to perform grid clustering at each level of the system to identify taxi travel feature regions of different scales. Subsequently, a multi-scale fusion pyramid model is constructed utilizing the acquired travel feature regions at various scales. Finally, we determine the transformation relationships between scales in the pyramid model, enabling the identification of travel feature areas across both known and unknown scales. This approach facilitates the recognition of travel features within cities at any scale. Experimental results demonstrate that the multi-scale fusion transformation algorithm enhances our understanding of spatial aggregation patterns and the hierarchical structure of geographical entities, offering a novel approach for multi-scale urban traffic analysis. © 2024 SPIE. All rights reserved.

关键词： Clustering algorithms

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Automatic Segmentation Method for the Nuclei Contour of High Resolution Rat Brain 23

Automatic Segmentation Method for the Nuclei Contour of High...

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6th International Conference on big data Technologies, ICBDT 2023

作者： Xu, Xiaofeng Zheng, Wei Henan Engineering Research Center of Intelligent Processing for Big Data of Digital Image School of Computer Science and Technology Nanyang Normal University Nanyang473061 China

ISBN: (纸本)9798400707667

The brain consists of massive nuclei with different functions. In neuroscience research, the precise recognition and delineation of nucleus boundaries is the crux of brain atlas illustration. Here, we propose a method based on Markov Random Field. This method introduces fractional differentiation into texture feature extraction and a new potential energy function is defined based on kernel region information. To handle the problem of large data volume, we propose a strategy of dual MRF processing with down sampling for pre classification. Finally, the fuzzy entropy criterion is used to optimize the segmentation results. Experiments on model data and real tissue slice data show that this method can not only segment the regions with obvious differences in the density of the nuclei with insignificant differences in the density of nuclei, but also divide the nuclei without insignificant density differences by the cell shapes and textures. In addition, this method can not only segment a specific target kernel in one calculation, but also simultaneously partition multiple target kernels. The segmentation algorithm shows great potential for illustrating high-resolution 3D brain atlases. © 2023 ACM.

关键词： Markov processes

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Prediction and Analysis of Stroke Risk Based on Ensemble Learning 15th

Prediction and Analysis of Stroke Risk Based on Ensemble Lea...

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15th International Conference on Genetic and Evolutionary Computing, ICGEC 2023

作者： Zuo, Xiuji Guo, Xin Yin, Zilong Tseng, Shih-Pang School of Information Science and Technology Sanda University Shanghai201209 China Department of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai201620 China School of Software and Big Data Changzhou College of Information Technology Changzhou213164 China

ISBN: (纸本)9789819994113

With the development of science and technology, the application of data mining in medical field is becoming more and more popular. Machine learning methods also plays an important role in disease prediction. Stroke is characterized by high incidence rate, high disability rate, high mortality rate and high recurrence rate, and it is also likely to cause other kinds of complications. In this paper, each feature in the stroke dataset was analyzed in order to find out the factors affecting stroke and conducts classification and prediction research on whether there is a disease risk. Specifically, the PCA (Principal Component Analysis) algorithm is used to extract the main feature components of data, the SMOTE (Synthetic Minority Oversampling Technique) algorithm is used to adjust imbalanced feature categories. Traditional machine learning classification algorithms, such as decision tree, SVM(support vector machines), and various ensemble learning algorithms are used for the prediction of stroke risk, so as to study the relationship between stroke disease and each feature, and the classification prediction model, so that we can prevent strokes in time and reduce the risk of stroke. Among all the models, Bagging (Bootstrap aggregating) has the best performance with an ROC value of 0.97. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Principal component analysis

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Distributed control for spatially interconnected time-varying delay systems under input saturation

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Journal of the Franklin Institute 2023年第4期360卷 3137-3161页

作者： Wang, Hui Xu, Huiling Chen, Xuefeng Ge, Shuzhi Sam Aliyun School of Big Data Changzhou University Jiangsu Changzhou213164 China School of Mathematics and Statistics Nanjing University of Science and Technology Jiangsu Nanjing210094 China Department of Electrical and Computer Engineering National University of Singapore 117576 Singapore

This paper presents the distributed control design for a class of spatially interconnected continuous-time time-varying delay (SICTD) systems under input saturation. A distributed controller and distributed anti-windup compensator (AWC) are proposed based on the distributed structure of the SICTD system. Then, a sufficient condition is derived to guarantee the asymptotic stability and H∞ performance of the closed-loop system under the saturation constraints. We have also provided an algorithm for obtaining the AWC parameters by employing the elimination lemma and the cone complementary linearization approach. The proposed anti-windup compensation methodology can also be employed to compensate for the actuator saturation of the spatially interconnected delay-free systems. Finally, two practical examples are presented to verify the effectiveness of the proposed AWC design method. © 2023 The Franklin Institute

关键词： Asymptotic stability

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End-to-End Aspect-Based Sentiment Analysis Based on IDCNN-BLSA Feature Fusion 22nd

End-to-End Aspect-Based Sentiment Analysis Based on IDCNN-B...

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22nd International Symposium on Knowledge and Systems sciences, KSS 2023

作者： Liu, Xinyuan Chen, Jindong Zhang, Wen Beijing Information Science and Technology University Beijing100192 China Intelligent Decision Making and Big Data Application Beijing International Science and Technology Cooperation Base Beijing China Beijing University of Technology Beijing China

ISBN: (纸本)9789819983179

The existing end-to-end Aspect-Based Sentiment Analysis (ABSA) algorithms focus on feature extraction by a single model, which leads to the loss of the important local or global information. In order to capture both local and global information of sentences, an end-to-end ABSA method based on features fusion is proposed. Firstly, the pre-trained model BERT is applied to obtain word vectors;secondly, Iterated Dilated Convolutions Neural Networks (IDCNN) and Bi-directional Long Short-Term Memory (BiLSTM) with Self-Attention mechanism (BLSA) are adopted to capture local and global features of sentences, and the generated local and context dependency vectors are fused to yield feature vectors. Finally, Conditional Random Fields (CRF) is applied to predict aspect words and sentiment polarity simultaneously. On Laptop14 and Restaurant datasets, our model’s F1 scores increased by 0.51%, 3.11% respectively compared with the best model in the comparison experiment, and 0.74%, 0.78% respectively compared with the single model with the best effect in the ablation experiment. We removed each important module in turn in subsequent experiments and compared it with our model. The experimental results demonstrate the effectiveness of this method in aspect word recognition and its better generalization ability. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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Design of a Virtual Simulation Experimental Teaching Platform Based on the Internet of Things 13

Design of a Virtual Simulation Experimental Teaching Platfor...

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13th International Conference on Information technology in Medicine and Education, ITME 2023

作者： Xiehua, Yu Shengjie, You Rentao, Lin School of Computer and Information MinNan Science and Technology University Quanzhou China University of Fujian Province Key Laboratory of Big Data and Artificial Intelligence Quanzhou China

ISBN: (纸本)9798350319156

The virtual simulation experiment teaching system based on the Internet of Things simulates the Internet of Things sensing device in the form of software, and integrates relatively new teaching methods and theoretical knowledge, designs and completes a characteristic, can adapt to various needs of the Internet of Things experiment teaching system, and strives to provide more efficient protection and help for teachers' teaching tasks and students' learning tasks. To a large extent, it can solve the problems in the design and implementation of the Internet of Things application system, provide students with practical operation opportunities in the way of virtual simulation, combine theory and practice, and cultivate application-oriented talents urgently needed by the industry. © 2023 IEEE.

关键词： Internet of things

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SteadySketch: Finding Steady Flows in data Streams 31

SteadySketch: Finding Steady Flows in Data Streams

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31st IEEE/ACM International Symposium on Quality of Service, IWQoS 2023

作者： Li, Xiaodong Fan, Zhuochen Li, Haoyu Zhong, Zheng Guo, Jiarui Long, Sheng Yang, Tong Cui, Bin School of Computer Science Peking University National Engineering Laboratory for Big Data Analysis Technology and Application Beijing China Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9798350399738

In this paper, we study steady flows in data streams, which refers to those flows whose arrival rate is always non-zero and around a fixed value for several consecutive time windows. To find steady flows in real time, we propose a novel sketch, SteadySketch, aiming to accurately report steady flows with limited memory. To the best of our knowledge, this is the first work to define and find steady flows in data streams. The key novelty of SteadySketch is our proposed reborn technique, which reduces the required memory space by 75%. Experimental results show that SteadySketch improves the Precision Rate (PR) by 81.1% and reduces the Average Relative Error (ARE) by 955.3× compared with the strawman solution. Finally, we provide a concrete case: cache prefetch, and prove that SteadySketch can effectively improve the cache hit ratio. All related codes of SteadySketch are open-source and available at GitHub. © 2023 IEEE.

关键词： Steady flow

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Scale‐wise interaction fusion and knowledge distillation network for aerial scene recognition

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CAAI Transactions on Intelligence technology 2023年第4期8卷 1178-1190页

作者： Hailong Ning Tao Lei Mengyuan An Hao Sun Zhanxuan Hu Asoke K.Nandi School of Computer Science and Technology Xi'an University of Posts and TelecommunicationsShaanxi Key Laboratory of Network Data Analysis and Intelligent ProcessingXi'anChina Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'anChina School of Electronic Information and Artificial Intelligence Shaanxi University of Science and TechnologyXi'anChina School of Computer Central China Normal UniversityWuhanChina Department of Electronic and Electrical Engineering Brunel University LondonLondonUK Xi'an Jiaotong University Xi'anChina

Aerial scene recognition(ASR)has attracted great attention due to its increasingly essential *** of the ASR methods adopt the multi‐scale architecture because both global and local features play great roles in ***,the existing multi‐scale methods neglect the effective interactions among different scales and various spatial locations when fusing global and local features,leading to a limited ability to deal with challenges of large‐scale variation and complex background in aerial scene *** addition,existing methods may suffer from poor generalisations due to millions of to‐belearnt parameters and inconsistent predictions between global and local *** tackle these problems,this study proposes a scale‐wise interaction fusion and knowledge distillation(SIF‐KD)network for learning robust and discriminative features with scaleinvariance and background‐independent *** main highlights of this study include two *** the one hand,a global‐local features collaborative learning scheme is devised for extracting scale‐invariance features so as to tackle the large‐scale variation problem in aerial scene ***,a plug‐and‐play multi‐scale context attention fusion module is proposed for collaboratively fusing the context information between global and local *** the other hand,a scale‐wise knowledge distillation scheme is proposed to produce more consistent predictions by distilling the predictive distribution between different scales during *** experimental results show the proposed SIF‐KD network achieves the best overall accuracy with 99.68%,98.74%and 95.47%on the UCM,AID and NWPU‐RESISC45 datasets,respectively,compared with state of the arts.

关键词： deep learning image analysis image classification information fusion

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Adaptive Prototype Network with Common and Discriminative Representation Learning for Few-Shot Relation Extraction 9th

Adaptive Prototype Network with Common and Discriminative ...

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

作者： Hu, Wenyue Zhong, Jiang Xia, Yang Zhou, Yangmei Li, Rongzhen Chongqing University Chongqing China Chongqing Academy of Big Data Co. Ltd. Chongqing China Chongqing Academy of Science and Technology Chongqing China

ISBN: (纸本)9783031466731

The task of few-shot relation extraction presents a significant challenge as it requires predicting the potential relationship between two entities based on textual data using only a limited number of labeled examples for training. Recently, quite a few studies have proposed to handle this task with task-agnostic and task-specific weights, among which prototype networks have proven to achieve the best performance. However, these methods often suffer from overfitting novel relations because every task is treated equally. In this paper, we propose a novel methodology for prototype representation learning in task-adaptive scenarios, which builds on two interactive features: 1) common features are used to rectify the biased representation and obtain the relative class-centered prototype as much as possible, and 2) discriminative features help the model better distinguish similar relations by the representation learning of the entity pairs and instances. We obtain the hybrid prototype representation by combining common and discriminative features to enhance the adaptability and recognizability of few-shot relation extraction. Experimental results on FewRel dataset, under various few-shot settings, showcase the improved accuracy and generalization capabilities of our model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Extraction

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HVAC Optimal Control Based on the Sensitivity Analysis:An Improved SA Combination Method Based on a Neural Network

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Computer Modeling in Engineering & sciences 2023年第9期136卷 2741-2758页

作者： Lifan Zhao Zetian Huang Qiming Fu Nengwei Fang Bin Xing Jianping Chen School of Electronic and Information Engineering Suzhou University of Science and TechnologySuzhou215009China Jiangsu Key Laboratory of Intelligent Building Energy Efficiency Suzhou University of Science and TechnologySuzhou215009China Suzhou Key Laboratory of Mobile Network Technology and Application Suzhou University of Science and TechnologySuzhou215009China Chongqing Industrial Big Data Innovation Center Co. Ltd.Chongqing400707China

Aiming at optimizing the energy consumption of HVAC,an energy conservation optimization method was proposed for HVAC systems based on the sensitivity analysis(SA),named the sensitivity analysis combination method(SAC).Based on the SA,neural network and the related settings about energy conservation of HVAC systems,such as cooling water temperature,chilled water temperature and supply air temperature,were ***,based on the data of the existing HVAC system,various optimal control methods ofHVAC systems were tested and evaluated by a simulated HVAC system in *** results show that the proposed SA combination method can reduce significant computational load while maintaining an equivalent energy performance compared with traditional optimal control methods.

关键词： Energy conservation sensitivity analysis HVAC system neural network

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