自主智能系统(英文)最新文献_第4页

Space-time video super-resolution using long-term temporal feature aggregation 基于长时间特征聚合的时空视频超分辨率

自主智能系统(英文) Pub Date : 2023-06-16 DOI: 10.1007/s43684-023-00051-9

Kuanhao Chen, Zijie Yue, Miaojing Shi

{"title":"Space-time video super-resolution using long-term temporal feature aggregation","authors":"Kuanhao Chen, Zijie Yue, Miaojing Shi","doi":"10.1007/s43684-023-00051-9","DOIUrl":"10.1007/s43684-023-00051-9","url":null,"abstract":"<div><p>Space-time video super-resolution (STVSR) serves the purpose to reconstruct high-resolution high-frame-rate videos from their low-resolution low-frame-rate counterparts. Recent approaches utilize end-to-end deep learning models to achieve STVSR. They first interpolate intermediate frame features between given frames, then perform local and global refinement among the feature sequence, and finally increase the spatial resolutions of these features. However, in the most important feature interpolation phase, they only capture spatial-temporal information from the most adjacent frame features, ignoring modelling long-term spatial-temporal correlations between multiple neighbouring frames to restore variable-speed object movements and maintain long-term motion continuity. In this paper, we propose a novel long-term temporal feature aggregation network (LTFA-Net) for STVSR. Specifically, we design a long-term mixture of experts (LTMoE) module for feature interpolation. LTMoE contains multiple experts to extract mutual and complementary spatial-temporal information from multiple consecutive adjacent frame features, which are then combined with different weights to obtain interpolation results using several gating nets. Next, we perform local and global feature refinement using the Locally-temporal Feature Comparison (LFC) module and bidirectional deformable ConvLSTM layer, respectively. Experimental results on two standard benchmarks, Adobe240 and GoPro, indicate the effectiveness and superiority of our approach over state of the art.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-023-00051-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44824026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Formation control of unmanned rotorcraft systems with state constraints and inter-agent collision avoidance 具有状态约束和避免代理间碰撞的无人驾驶旋翼机系统的编队控制

自主智能系统(英文) Pub Date : 2023-05-09 DOI: 10.1007/s43684-023-00049-3

Panpan Zhou, Shupeng Lai, Jinqiang Cui, Ben M. Chen

{"title":"Formation control of unmanned rotorcraft systems with state constraints and inter-agent collision avoidance","authors":"Panpan Zhou, Shupeng Lai, Jinqiang Cui, Ben M. Chen","doi":"10.1007/s43684-023-00049-3","DOIUrl":"10.1007/s43684-023-00049-3","url":null,"abstract":"<div><p>We present in this paper a novel framework and distributed control laws for the formation of multiple unmanned rotorcraft systems, be it single-rotor helicopters or multi-copters, with physical constraints and with inter-agent collision avoidance, in cluttered environments. The proposed technique is composed of an analytical distributed consensus control solution in the free space and an optimization based motion planning algorithm for inter-agent and obstacle collision avoidance. More specifically, we design a distributed consensus control law to tackle a series of state constraints that include but not limited to the physical limitations of velocity, acceleration and jerk, and an optimization-based motion planning technique is utilized to generate numerical solutions when the consensus control fails to provide a collision-free trajectory. Besides, a sufficiency condition is given to guarantee the stability of the switching process between the consensus control and motion planning. Finally, both simulation and real flight experiments successfully demonstrate the effectiveness of the proposed technique.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-023-00049-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126930221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel collaborative decision-making method based on generalized abductive learning for resolving design conflicts 一种基于广义溯因学习的解决设计冲突的协同决策新方法

自主智能系统(英文) Pub Date : 2023-02-28 DOI: 10.1007/s43684-023-00048-4

Zhexin Cui, Jiguang Yue, Wei Tao, Qian Xia, Chenhao Wu

{"title":"A novel collaborative decision-making method based on generalized abductive learning for resolving design conflicts","authors":"Zhexin Cui, Jiguang Yue, Wei Tao, Qian Xia, Chenhao Wu","doi":"10.1007/s43684-023-00048-4","DOIUrl":"10.1007/s43684-023-00048-4","url":null,"abstract":"<div><p>In complex product design, lots of time and resources are consumed to choose a preference-based compromise decision from non-inferior preliminary design models with multi-objective conflicts. However, since complex products involve intensive multi-domain knowledge, preference is not only a comprehensive representation of objective data and subjective knowledge but also characterized by fuzzy and uncertain. In recent years, enormous challenges are involved in the design process, within the increasing complexity of preference. This article mainly proposes a novel decision-making method based on generalized abductive learning (G-ABL) to achieve autonomous and efficient decision-making driven by data and knowledge collaboratively. The proposed G-ABL framework, containing three cores: classifier, abductive kernel, and abductive machine, supports preference integration from data and fuzzy knowledge. In particular, a subtle improvement is presented for WK-means based on the entropy weight method (EWM) to address the local static weight problem caused by the fixed data preferences as the decision set is locally invariant. Furthermore, fuzzy comprehensive evaluation (FCE) and <i>Pearson</i> correlation are adopted to quantify domain knowledge and obtain abducted labels. Multi-objective weighted calculations are utilized only to label and compare solutions in the final decision set. Finally, an engineering application is provided to verify the effectiveness of the proposed method, and the superiority of which is illustrated by comparative analysis.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-023-00048-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49219365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Approach for improved development of advanced driver assistance systems for future smart mobility concepts 为未来智能移动概念改进先进驾驶辅助系统开发的方法

自主智能系统(英文) Pub Date : 2023-02-27 DOI: 10.1007/s43684-023-00047-5

Michael Weber, Tobias Weiss, Franck Gechter, Reiner Kriesten

引用次数: 0

Output-based adaptive distributed observer for general linear leader systems over periodic switching digraphs 周期性开关数字图上一般线性领导者系统的基于输出的自适应分布式观测器

自主智能系统(英文) Pub Date : 2023-02-20 DOI: 10.1007/s43684-023-00046-6

Changran He, Jie Huang

引用次数: 0

Multi-agent reinforcement learning for autonomous vehicles: a survey 自动驾驶汽车的多智能体强化学习研究

自主智能系统(英文) Pub Date : 2022-11-16 DOI: 10.1007/s43684-022-00045-z

Joris Dinneweth, Abderrahmane Boubezoul, René Mandiau, Stéphane Espié

{"title":"Multi-agent reinforcement learning for autonomous vehicles: a survey","authors":"Joris Dinneweth, Abderrahmane Boubezoul, René Mandiau, Stéphane Espié","doi":"10.1007/s43684-022-00045-z","DOIUrl":"10.1007/s43684-022-00045-z","url":null,"abstract":"<div><p>In the near future, autonomous vehicles (AVs) may cohabit with human drivers in mixed traffic. This cohabitation raises serious challenges, both in terms of traffic flow and individual mobility, as well as from the road safety point of view. Mixed traffic may fail to fulfill expected security requirements due to the heterogeneity and unpredictability of human drivers, and autonomous cars could then monopolize the traffic. Using multi-agent reinforcement learning (MARL) algorithms, researchers have attempted to design autonomous vehicles for both scenarios, and this paper investigates their recent advances. We focus on articles tackling decision-making problems and identify four paradigms. While some authors address mixed traffic problems with or without social-desirable AVs, others tackle the case of fully-autonomous traffic. While the latter case is essentially a communication problem, most authors addressing the mixed traffic admit some limitations. The current human driver models found in the literature are too simplistic since they do not cover the heterogeneity of the drivers’ behaviors. As a result, they fail to generalize over the wide range of possible behaviors. For each paper investigated, we analyze how the authors formulated the MARL problem in terms of observation, action, and rewards to match the paradigm they apply.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-022-00045-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47231282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep learning prediction of amplitude death 深度学习预测振幅死亡

自主智能系统(英文) Pub Date : 2022-11-15 DOI: 10.1007/s43684-022-00044-0

Pengcheng Ji, Tingyi Yu, Yaxuan Zhang, Wei Gong, Qingyun Yu, Li Li

引用次数: 0

Urban rail transit FAO system: technological development and trends 城市轨道交通FAO系统：技术发展和趋势

自主智能系统(英文) Pub Date : 2022-10-28 DOI: 10.1007/s43684-022-00043-1

Tao Tang, Wentao Liu, Shukui Ding, Chunhai Gao, Shuai Su

引用次数: 0

Forecasting the yield of wafer by using improved genetic algorithm, high dimensional alternating feature selection and SVM with uneven distribution and high-dimensional data 采用改进的遗传算法、高维交替特征选择和支持向量机对分布不均匀的高维数据进行晶圆产量预测

自主智能系统(英文) Pub Date : 2022-09-26 DOI: 10.1007/s43684-022-00041-3

Qiuhao Xu, Chuqiao Xu, Junliang Wang

{"title":"Forecasting the yield of wafer by using improved genetic algorithm, high dimensional alternating feature selection and SVM with uneven distribution and high-dimensional data","authors":"Qiuhao Xu, Chuqiao Xu, Junliang Wang","doi":"10.1007/s43684-022-00041-3","DOIUrl":"10.1007/s43684-022-00041-3","url":null,"abstract":"<div><p>Wafer yield prediction, as the basis of quality control, is dedicated to predicting quality indices of the wafer manufacturing process. In recent years, data-driven machine learning methods have received a lot of attention due to their accuracy, robustness, and convenience for the prediction of quality indices. However, the existing studies mainly focus on the model level to improve the accuracy of yield prediction does not consider the impact of data characteristics on yield prediction. To tackle the above issues, a novel wafer yield prediction method is proposed, in which the improved genetic algorithm (IGA) is an under-sampling method, which is used to solve the problem of data overlap between finished products and defective products caused by the similarity of manufacturing processes between finished products and defective products in the wafer manufacturing process, and the problem of data imbalance caused by too few defective samples, that is, the problem of uneven distribution of data. In addition, the high-dimensional alternating feature selection method (HAFS) is used to select key influencing processes, that is, key parameters to avoid overfitting in the prediction model caused by many input parameters. Finally, SVM is used to predict the yield. Furthermore, experiments are conducted on a public wafer yield prediction dataset collected from an actual wafer manufacturing system. IGA-HAFS-SVM achieves state-of-art results on this dataset, which confirms the effectiveness of IGA-HAFS-SVM. Additionally, on this dataset, the proposed method improves the AUC score, G-Mean and F1-score by 21.6%, 34.6% and 0.6% respectively compared with the conventional method. Moreover, the experimental results prove the influence of data characteristics on wafer yield prediction.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-022-00041-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46477594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

New aggregation functions for spherical fuzzy sets and the spherical fuzzy distance within the MULTIMOORA method with applications MULTIMOORA方法中球面模糊集和球面模糊距离的新聚合函数及其应用

自主智能系统(英文) Pub Date : 2022-09-20 DOI: 10.1007/s43684-022-00042-2

Iman Mohamad Sharaf

{"title":"New aggregation functions for spherical fuzzy sets and the spherical fuzzy distance within the MULTIMOORA method with applications","authors":"Iman Mohamad Sharaf","doi":"10.1007/s43684-022-00042-2","DOIUrl":"10.1007/s43684-022-00042-2","url":null,"abstract":"<div><p>This article develops a novel approach for multi-objective optimization on the basis of ratio analysis plus the full multiplicative form (MULTIMOORA) using spherical fuzzy sets (SFSs) to obtain proper evaluations. SFSs surpass Pythagorean and intuitionistic fuzzy sets in modeling human cognition since the degree of hesitation is expressed explicitly in a three-dimensional space. In the spherical fuzzy environment, the implementation of the MULTIMOORA encounters two major problems in the aggregation operators and the distance measures that might lead to erroneous results. The extant aggregation operators in some cases can result in a biased evaluation. Therefore, two aggregation functions for SFSs are proposed. These functions guarantee balanced evaluation and avoid false ranking. In the reference point technique, when comparing SFSs, being closer to the ideal solution does not necessarily imply an SFS with a better score. To make up for this drawback, two reference points are employed instead of one, and the distance is not expressed as a crisp value but as an SFS instead. To overcome the disadvantages of the dominance theory in large-scale applications, the results of the three techniques are aggregated to get the overall utility on which the ranking is based. The illustration and validation of the proposed spherical fuzzy MULTIMOORA are examined through two applications, personnel selection, and energy storage technologies selection. The results are compared with the results of other methods to explicate the adequacy of the proposed method and validate the results.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-022-00042-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48565853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0