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第33届中国控制与决策会议

作者： Yali Wu Liting Huang Jinjin Qi Xiaoxiao Quan School of Automation and Information Engineering Xi'an University of Technology Shaanxi ProvinceKeyLaboratory of Complex System Control and Intelligent Information Processing Shaanxi Province Key Laboratory of Complex System Control and Intelligent Information Processing

ISBN: (纸本)9781665431293

To solve the problem of low performance of network intrusion detection,a deep learning intrusion detection model based on space-time fusion features and attention mechanism—CLT-net is *** this model,space-time fusion features are obtained by integrating convolutional neural network and long short-time memory network,and attention module is added to calculate the importance of the input features,and softmax function is used for *** a large number of simulation experiments on NSL-KDD data sets,CLT-net has significantly improved the convergence of the training set and the accuracy of the test *** with the traditional CNN model with similar structure and the space-time fusion CLSTM the accuracy of the model increased by 11.8% and 10.9%*** shows that this model has great potential in the application field of network intrusion detection.

关键词： Network Intrusion Detection Convolution Neural Network Long Short-term Memory Network Attention Mechanism CLT-net

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Spatio-Temporal Context Modeling for Road Obstacle Detection 1

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Wu, Xiuen Wang, Tao Liang, Lingyu Li, Zuoyong Ching, Fum Yew College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350108 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Wuyishan354300 China School of Electronic and Information Engineering South China University of Technology Guangzhou510641 China Ministry of Education Key Laboratory of Computer Network and Information Integration Southeast University Nanjing211189 China Guangzhou510320 China Penang11800 Malaysia

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

Road obstacle detection is an important problem for vehicle driving safety. In this paper, we aim to obtain robust road obstacle detection based on spatio-temporal context modeling. Firstly, a data-driven spatial context model of the driving scene is constructed with the layouts of the training data. Then, obstacles in the input image are detected via the state-of-the-art object detection algorithms, and the results are combined with the generated scene layout. In addition, to further improve the performance and robustness, temporal information in the image sequence is taken into consideration, and the optical flow is obtained in the vicinity of the detected objects to track the obstacles across neighboring frames. Qualitative and quantitative experiments were conducted on the Small Obstacle Detection (SOD) dataset and the Lost and Found dataset. The results indicate that our method with spatio-temporal context modeling is superior to existing methods for road obstacle detection. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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Research on emotion classification of multimodal physiological signals based on cross group convolution neural network

Research on emotion classification of multimodal physiologic...

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2024 International Conference on Image, Signal processing, and Pattern Recognition, ISPP 2024

作者： Zhuan, Zhikun Bian, Yan Zhang, Zhiwen Wang, Yuanchao Yang, Yang Geng, Liqing Sun, Miao Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin University of Technology and Education Tianjin300222 China Beijing University of Technology Beijing100124 China

ISBN: (纸本)9781510680425

Emotion recognition from physiological signals is a crucial area in affective computing. However, traditional CNN models face challenges in accuracy and efficiency. This paper proposes a lightweight IGC-CNN model that integrates interleaved group convolutions with the LeNet-5 network. Experimental results using EEG, EMG, and EDA signals collected across happiness, sadness, and fear states show that IGC-CNN achieves an average accuracy of 94.74%, outperforming traditional CNNs by 10.06%. Statistical analysis confirms the significance of this improvement (P © 2024 SPIE.

关键词： Convolutional neural networks

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NeFF: Semantic information Learning In 3D Space Using Neural Feature Field

NeFF: Semantic Information Learning In 3D Space Using Neural...

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2024 China Automation Congress, CAC 2024

作者： Yang, Yimu Chen, Cailian Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368604

Inferring a 3D space with partial observation requires prior knowledge from previous experience, and deep learning offers an intuitive solution. Prior knowledge can be categorized into two extremes Global and Local. Global prior uses input image as a clue to retrieve seen intact 3D objects yielding 3D consistent inference with fewer details. In contrast, Local prior takes advantage of closeby views’ overlapping areas and propagates input image cues to their vicinity via geometry transformation directly, producing realistic inferences around the reference views yet inconsistent inferences away from the reference views. We propose Neural feature field, an implicit 3D neural representation that combines the strength of the Global and Local prior. We represent each 3D scene with a latent embedding, then we perform a global scene-level prior to local view-specific feature prior with the neural feature field and volume rendering technique. Our model learns general scene properties across a category. Given a single input image at test time, the scene latent code is acquired via a two-round optimization process. Experiments on the SRN benchmark show that NeFF generalizes well to unseen objects within the same object category and is capable of producing 3D consistent inferences with sharp details from a single observation. © 2024 IEEE.

关键词： 3D reconstruction

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Empowerment of Precise Ideological and Political Education in Higher Education with Educational Digitalization

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Journal of Contemporary Educational Research 2024年第4期8卷 23-28页

作者： Yan Dong Yu Yu Ge Yao Ulanqab Key Laboratory of Intelligent Information Processing and Security Jining Normal UniversityUlanqab 012000China

The study explores how educational digitalization enables the precise development of ideological and political education in colleges and *** transformation enables colleges and universities to accurately define educational objectives,content strategies,effect evaluation,and process management,and realize the precision and intelligence of ideological and political *** application of big data technology enhances the data-oriented thinking of teachers and students,promotes the accurate application of data,and improves the efficiency of ideological and political educa*** research also prospected a new vision of the digital construction of ideological and political courses and clarified the theoretical and practical path of the implementation and evaluation mode of ideological and political courses under digital *** digitalization enables precise ideological and political education,which is a key way to promote the innovative development of ideological and political education in colleges and universities and will strongly support the improvement of the overall quality of higher education and the training of excellent talents.

关键词： Education digital empowerment Precise ideological and political education Improvement of efficiency

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Dual-type Hyperspectral Microscopic Imaging System based on Fourier Transform Imaging Spectrometer 15

Dual-type Hyperspectral Microscopic Imaging System based on ...

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15th International Conference on information Optics and Photonics, CIOP 2024

作者： Wei, Zhanqi Wu, Kaijie Qin, Yuheng Cheng, Hao Gu, Chaochen Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

ISBN: (数字)9781510686175

ISBN: (纸本)9781510686168

This article proposes a hyperspectral microscopic imaging system based on Fourier transform interferometer to broaden the spectral range and obtain more types of spectral information under large field of view. Through stage scanning and light sources switching, transmission and fluorescence spectral images can be obtained successively. The system was validated using fibre tissues imaging. The spectral range covers 300-800 nm, and the spectral resolution is approximately 5 nm. It can be effectively applied to pathology and cytogenetics, obtaining morphological and spectral information to study the structure of the object and observe the similarities and differences of components in different positions. © 2024 SPIE.

关键词： Spectral resolution

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A Fuzzy Creative Works Generation Algorithm Based on Graph Neural Network 6th

A Fuzzy Creative Works Generation Algorithm Based on Graph N...

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6th Euro-China Conference on intelligent Data Analysis and Applications, ECC 2019

作者： Zhang, Fuquan Wang, Yiou Zhao, Guifen Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China Beijing Institute of Science and Technology Information Beijing100044 China

ISBN: (纸本)9789811650352

A fuzzy creative works generation algorithm based on graph neural network is proposed. Firstly, the multi-label fuzzy creative data set is constructed. Secondly, fuzzy logical correlations between creative objects are dynamically calculated by graphical neural networks to capture relevant digital objects and their relationships. Thirdly, the projectiles, boundary markers and location words of the creative scene objects are generated by analyzing related attributes of each entity based on graph neural network. Fuzzy creative works are automatically generated by adjusting the appropriate location. Finally, the experiments are deployed, and experimental results show that the accuracy rate of the proposed algorithm is improved by 9.6% on average compared with the manual selection, which indicates the feasibility of the proposed algorithm replacing the manual selection. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fuzzy neural networks

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Hotkey-Cache: A Hotkey-Separate Cache to Improve Performance for key-Value Store 2

HotKey-Cache: A HotKey-Separate Cache to Improve Performance...

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2nd International Conference on Cloud Computing, Big Data Application and Software Engineering, CBASE 2023

作者： Lu, Jinkang Xie, Ping Tibetan Intelligent Information Processing and Application The State Key Laboratory Xining China Qinghai Province Key Laboratory of Internet of Things Xining China Academy of Plateau Science and Sustainability Xining China

ISBN: (纸本)9798350331448

In most key-value stores, data access patterns are typically non-uniform and display skewness, resulting in the presence of hotkeys and coldkeys. The caching mechanism of key-value stores, which relies on the Least Recently Used (LRU) policy like LRUCache, only accounts for the temporal relevance of the data. In addition, the caching mechanism based on the Least Frequently Used (LFU) policy like LFUCache, only considers the frequency of data access. The limitations of these caching mechanisms lead to a reduction in cache efficiency. This paper presents a novel caching mechanism called Hotkey-Cache, which focuses on separating hotkeys. Building upon LRUCache, Hotkey-Cache separate hotkeys and assign them to a specialized queue to prevent their direct eviction. This design simultaneously considers both the frequency of data access and the temporal relevance of the data. Finally, we deploy these caches in LevelDB. Experimental results indicated a 12.67% enhancement compared to LRUCache and a 28.52% improvement compared to LFUCache. © 2023 IEEE.

关键词： cache frequency of access key-value store temporal relevance

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A surface defect detection method for steel pipe based on improved YOLO

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Mathematical Biosciences and Engineering 2024年第2期21卷 3016-3036页

作者： Wang, Lili Song, Chunhe Wan, Guangxi Cui, Shijie State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China College of Information Liaoning University Shenyang110036 China

Surface defect detection is of great significance as a tool to ensure the quality of steel pipes. The surface defects of steel pipes are charactered by insufficient texture, high similarity between different types of defects, large size differences, and high proportions of small targets, posing great challenges to defect detection algorithms. To overcome the above issues, we propose a novel steel pipe surface defect detection method based on the YOLO framework. First, for the problem of a low detection rate caused by insufficient texture and high similarity among different types of defects of steel pipes, a new backbone block is proposed. By increasing high-order spatial interaction and enhancing the capture of internal correlations of data features, different feature information for similar defects is extracted, thereby alleviating the false detection rate. Second, to enhance the detection performance for small defects, a new neck block is proposed. By fusing multiple features, the accuracy of steel pipe defect detection is improved. Third, for the problem of a low detection rate causing large size differences in steel pipe surface defects, a novel regression loss function that considers the aspect ratio and scale is proposed, and the focal loss is introduced to further solve the sample imbalance problem in steel pipe defect datasets. The experimental results show that the proposed method can effectively improve the accuracy of steel pipe surface defect detection. © 2024 American Institute of Mathematical Sciences. All rights reserved.

关键词： Aspect ratio

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Evolutionary Multi-Objective Neural Architecture Search for Generalized Cognitive Diagnosis Models 5

Evolutionary Multi-Objective Neural Architecture Search for ...

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5th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2023

作者： Yang, Shangshang Zhen, Cheng Tian, Ye Ma, Haiping Liu, Yuanchao Zhang, Panpan Zhang, Xingyi School of Artificial Intelligence Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei China Institutes of Physical Science and Information Technology Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei China Automation for Process Industries Northeastern University State Key Laboratory of Synthetical Shenyang China

ISBN: (纸本)9798350393521

Cognitive diagnosis models (CDMs) with high generalization are essential for intelligent education systems to reveal students' knowledge states in multiple datasets. However, existing CDMs' architectures are designed dependent on researcher expertise and experience from observing and summarizing partial students' learning behaviors, which makes handcrafted models not cover all learning behaviors well and thus limits their generalization. To develop generalized CDMs, this paper proposes an evolutionary neural architecture search to design CDMs' architectures effective on multiple datasets automatically. Specifically, we first formulate the search task as a multi-objective optimization problem (MOP), which maximizes model performance on multiple datasets containing learning behaviors as many as possible to ensure model generalization. Then, an expressive search space is devised to contain as many potential architectures as possible, where each architecture is denoted by a unified form, taking three given inputs and integrating them in a linear or no-linear manner for prediction. Finally, an evolutionary algorithm with a tailored deduplication strategy is employed to solve the MOP, where each architecture is further represented by a single-root computation tree for easy optimization. Experiments on multiple datasets demonstrate the generalization and effectiveness of the best architecture searched by the proposed approach, and the searched architecture also holds as good interpretability as handcrafted architectures. © 2023 IEEE.

关键词： Multiobjective optimization

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