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Online control for pressure regulation of oxygen mask based on neural network

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Control Theory and Technology 2024年第3期22卷 487-501页

作者： Ligan Zhao Qinglin Sun Hao Sun Jin Tao Zengqiang Chen college of artificial intelligence Nankai UniversityTianjin 300350China Silo Al Helsinki 00100Finland

The aviation oxygen mask,which has a small volume of less than 1L and strong air tightness,imposes extremely high requirements on control performance of the oxygen *** on analyses of the operation principle of oxygen supply system,the dynamic model is established through the combination of mechanism analysis and experimental *** that the traditional fixed-parameter controllers are difficult to meet the control requirements with changes in pulmonary ventilation,this paper presents an online feedback controller based on neural network compensation(NNC),with connection weights that can be updated without *** mathematical simulations at different respiratory parameters,such as respiratory rate,are performed to verify the superior lower inspiratory resistance of controller with *** terms of hardware,an embedded AI control platform is to complete the experimental ***,the work may have downward compatibility to achieve stable oxygen supply in civil fields,such as medical ventilators,high-altitude expeditions.

关键词： Neural network Online control Pressure regulation AI platform

Adaptive interaction driven by the learning effect in the spatial prisoner's dilemma

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Chinese Physics B 2024年第3期33卷 160-169页

作者：李佳奇张建磊刘群 Institute of Intelligent Information Hexi UniversityGansu 734000China college of artificial intelligence Nankai UniversityTianjin 300350China

We propose a computing model in which individuals can automatically adjust their interaction intensity with their mentor according to the learning *** model is designed to investigate the cooperative dynamics of the spatial prisoner's *** specifically,when the cumulative payoff of a learner is more than his reference earning,he will strengthen his interaction with his mentor;otherwise,he will reduce *** experimental results indicate that this mechanism can improve the emergence of cooperation in a networked population and that the driving coefficient of interaction intensity plays an important role in promoting ***,under a certain social dilemma condition,there exists a minimal driving coefficient that leads to optimal *** occurs due to a positive feedback effect between the individual's satisfaction frequency and the number of effective ***,we find that the experimental results are in accord with theoretical predictions obtained from an extension of the classical pair-approximation *** conclusions obtained by considering relationships with mentors can provide a new perspective for future investigations into the dynamics of evolutionary games within structured populations.

关键词： self-adapting interaction evolutionary game mentor spatial prisoner's dilemma

DepthGAN: GAN-based depth generation from semantic layouts

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Computational Visual Media 2024年第3期10卷 505-522页

作者： Yidi Li Jun Xiao Yiqun Wang Zhengda Lu School of artificial intelligence University of Chinese Academy of SciencesBeijingChina college of Computer Science Chongqing UniversityChongqingChina

Existing GAN-based generative methodsare typically used for semantic image synthesis. Wepose the question of whether GAN-based architecturescan generate plausible depth maps and find thatexisting methods have difficulty in generating depthmaps which reasonably represent 3D scene structuredue to the lack of global geometric ***, we propose DepthGAN, a novel method ofgenerating a depth map using a semantic layout asinput to aid construction, and manipulation of wellstructured 3D scene point clouds. Specifically, wefirst build a feature generation model with a cascadeof semantically-aware transformer blocks to obtaindepth features with global structural *** our semantically aware transformer block, wepropose a mixed attention module and a semanticallyaware layer normalization module to better exploitsemantic consistency for depth features ***, we present a novel semantically weighteddepth synthesis module, which generates adaptivedepth intervals for the current scene. We generate thefinal depth map by using a weighted combination ofsemantically aware depth weights for different depthranges. In this manner, we obtain a more accuratedepth map. Extensive experiments on indoor andoutdoor datasets demonstrate that DepthGAN achievessuperior results both quantitatively and visually for thedepth generation task.

关键词： depth map generation generative model transformer scene generation

Adaptive Event-Triggered Time-Varying Output Group Formation Containment Control of Heterogeneous Multiagent Systems

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1398-1409页

作者： Lihong Feng Bonan Huang Jiayue Sun Qiuye Sun Xiangpeng Xie college of Information Science and Engineering Northeastern UniversityShenyang 110819China college of Automation and college of artificial intelligence Nanjing University of Posts and TelecommunicationsNanjing 210023China IEEE

In this paper,a class of time-varying output group formation containment control problem of general linear hetero-geneous multiagent systems(MASs)is investigated under directed *** MAS is composed of a number of tracking leaders,formation leaders and followers,where two different types of leaders are used to provide reference trajectories for movement and to achieve certain formations,***,compen-sators are designed whose states are estimations of tracking lead-ers,based on which,a controller is developed for each formation leader to accomplish the expected ***,two event-triggered compensators are proposed for each follower to evalu-ate the state and formation information of the formation leaders in the same group,***,a control protocol is designed for each follower,utilizing the output information,to guide the output towards the convex hull generated by the forma-tion leaders within the ***,the triggering sequence in this paper is decomposed into two sequences,and the inter-event intervals of these two triggering conditions are provided to rule out the Zeno ***,a numerical simulation is intro-duced to confirm the validity of the proposed results.

关键词： Adaptive control event-triggered mechanisms for-mation containment(FC) heterogeneous multiagent systems time-varying group formation.

Scattering-based hybrid network for facial attribute classification

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Frontiers of Computer Science 2024年第3期18卷 105-116页

作者： Na LIU Fan ZHANG Liang CHANG Fuqing DUAN School of artificial intelligence Beijing Normal UniversityBeijing 100875China

Face attribute classification(FAC)is a high-profile problem in biometric verification and face *** recent research has been devoted to extracting more delicate image attribute features and exploiting the inter-attribute correlations,significant challenges still *** scattering transform(WST)is a promising non-learned feature *** has been shown to yield more discriminative representations and outperforms the learned representations in certain *** to the image classification task,WST can enhance subtle image texture information and create local deformation *** paper designs a scattering-based hybrid block,to incorporate frequency-domain(WST)and image-domain features in a channel attention manner(Squeezeand-Excitation,SE),termed WS-SE *** with CNN,WS-SE achieves a more efficient FAC performance and compensates for the model sensitivity of the small-scale affine *** addition,to further exploit the relationships among the attribute labels,we propose a learning strategy from a causal *** cause attributes defined using the causalityrelated information can be utilized to infer the effect attributes with a high confidence *** analysis experiments demonstrate the effectiveness of our model,and our hybrid model obtains state-of-the-art results in two public datasets.

关键词： wavelet scattering transform causality-related learning facial attribute classification

A multiscale adaptive framework based on convolutional neural network: Application to fluid catalytic cracking product yield prediction

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Petroleum Science 2024年第4期21卷 2849-2869页

作者： Nan Liu Chun-Meng Zhu Meng-Xuan Zhang Xing-Ying Lan State Key Laboratory of Heavy Oil Processing China University of Petroleum (Beijing) college of artificial intelligence China University of Petroleum (Beijing)

Since chemical processes are highly non-linear and multiscale, it is vital to deeply mine the multiscale coupling relationships embedded in the massive process data for the prediction and anomaly tracing of crucial process parameters and production indicators. While the integrated method of adaptive signal decomposition combined with time series models could effectively predict process variables, it does have limitations in capturing the high-frequency detail of the operation state when applied to complex chemical processes. In light of this, a novel Multiscale Multi-radius Multi-step Convolutional Neural Network(Msrt Net) is proposed for mining spatiotemporal multiscale information. First, the industrial data from the Fluid Catalytic Cracking(FCC) process decomposition using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN) extract the multi-energy scale information of the feature subset. Then, convolution kernels with varying stride and padding structures are established to decouple the long-period operation process information encapsulated within the multi-energy scale data. Finally, a reconciliation network is trained to reconstruct the multiscale prediction results and obtain the final output. Msrt Net is initially assessed for its capability to untangle the spatiotemporal multiscale relationships among variables in the Tennessee Eastman Process(TEP). Subsequently, the performance of Msrt Net is evaluated in predicting product yield for a 2.80 × 10～6 t/a FCC unit, taking diesel and gasoline yield as examples. In conclusion, Msrt Net can decouple and effectively extract spatiotemporal multiscale information from chemical process data and achieve a approximately reduction of 30% in prediction error compared to other time-series models. Furthermore, its robustness and transferability underscore its promising potential for broader applications.

关键词： Fluid catalytic cracking Product yield Data-driven modeling Multiscale prediction Data decomposition Convolution neural network

Orientation and Decision-Making for Soccer Based on Sports Analytics and AI:A Systematic Review

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 37-57页

作者： Zhiqiang Pu Yi Pan Shijie Wang Boyin Liu Min Chen Hao Ma Yixiong Cui the School of artificial intelligence University of Chinese Academy of Sciences the Institute of Automation Chinese Academy of Sciences the Nanjing artificial intelligence Research of IA the School of Sports Engineering Beijing Sport University

Due to ever-growing soccer data collection approaches and progressing artificial intelligence(AI) methods, soccer analysis, evaluation, and decision-making have received increasing interest from not only the professional sports analytics realm but also the academic AI research community. AI brings gamechanging approaches for soccer analytics where soccer has been a typical benchmark for AI research. The combination has been an emerging topic. In this paper, soccer match analytics are taken as a complete observation-orientation-decision-action(OODA) *** addition, as in AI frameworks such as that for reinforcement learning, interacting with a virtual environment enables an evolving model. Therefore, both soccer analytics in the real world and virtual domains are discussed. With the intersection of the OODA loop and the real-virtual domains, available soccer data, including event and tracking data, and diverse orientation and decisionmaking models for both real-world and virtual soccer matches are comprehensively reviewed. Finally, some promising directions in this interdisciplinary area are pointed out. It is claimed that paradigms for both professional sports analytics and AI research could be combined. Moreover, it is quite promising to bridge the gap between the real and virtual domains for soccer match analysis and decision-making.

关键词： artificial intelligence(AI) decision-making football review soccer sports analytics

RepBoTNet-CESA:An Alzheimer’s Disease Computer Aided Diagnosis Method Using Structural Reparameterization BoTNet and Cubic Embedding Self Attention

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Computers, Materials & Continua 2024年第5期79卷 2879-2905页

作者： Xiabin Zhang Zhongyi Hu Lei Xiao Hui Huang college of Computer Science and artificial intelligence Wenzhou UniversityWenzhou325035China Key Laboratory of intelligence Image Processing and Analysis Wenzhou UniversityWenzhou325035China

Various deep learning models have been proposed for the accurate assisted diagnosis of early-stage Alzheimer’s disease(AD).Most studies predominantly employ Convolutional Neural Networks(CNNs),which focus solely on local features,thus encountering difficulties in handling global *** contrast to natural images,Structural Magnetic Resonance Imaging(sMRI)images exhibit a higher number of channel ***,during the Position Embedding stage ofMulti Head Self Attention(MHSA),the coded information related to the channel dimension is *** tackle these issues,we propose theRepBoTNet-CESA network,an advanced AD-aided diagnostic model that is capable of learning local and global features *** combines the advantages of CNN networks in capturing local information and Transformer networks in integrating global information,reducing computational costs while achieving excellent classification ***,it uses the Cubic Embedding Self Attention(CESA)proposed in this paper to incorporate the channel code information,enhancing the classification performance within the Transformer ***,the RepBoTNet-CESA performs well in various AD-aided diagnosis tasks,with an accuracy of 96.58%,precision of 97.26%,and recall of 96.23%in the AD/NC task;an accuracy of 92.75%,precision of 92.84%,and recall of 93.18%in the EMCI/NC task;and an accuracy of 80.97%,precision of 83.86%,and recall of 80.91%in the AD/EMCI/LMCI/NC *** demonstrates that RepBoTNet-CESA delivers outstanding outcomes in various AD-aided diagnostic ***,our study has shown that MHSA exhibits superior performance compared to conventional attention mechanisms in enhancing ResNet ***,the Deeper RepBoTNet-CESA network fails to make further progress in AD-aided diagnostic tasks.

关键词： Alzheimer CNN structural reparameterization multi head self attention computer aided diagnosis

Short-time maritime target detection based on polarization scattering characteristics

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Journal of Systems Engineering and Electronics 2024年第1期35卷 55-64页

作者： CHEN Shichao LUO Feng TIAN Min LYU Wanghan college of Computer and Information Engineering(college of artificial intelligence) Nanjing Tech UniversityNanjing 211816China Hangzhou Institute of Technology Xidian UniversityHangzhou 311200China

In this paper,a detection method combining Cameron decomposition based on polarization scattering characteristics in sea clutter background is ***,the Cameron decomposition is exploited to fuse the radar echoes of full polarization channels at the data *** to the artificial material structure on the surface of the target,it can be shown that the non-reciprocity of the target cell is stronger than that of the clutter ***,based on the analysis of the decomposition results,a new feature with scattering geometry characteristics in polarization domain,denoted as Cameron polarization decomposition scattering weight(CPD-SW),is extracted as the test statistic,which can achieve more detailed descriptions of the clutter scattering characteristics utilizing the difference between their scattering ***,the superiority of the proposed CPD-SW detector over traditional detectors in improving detection performance is verified by the IPIX measured dataset,which has strong stability under short-time observation in threshold detection and can also improve the separability of feature space zin anomaly detection.

关键词： sea clutter small target radar detection Cameron decomposition characteristics analysis