自主智能系统(英文)最新文献_第3页

Prediction for nonlinear time series by improved deep echo state network based on reservoir states reconstruction 基于储层状态重构的改进型深度回波态网络的非线性时间序列预测

自主智能系统(英文) Pub Date : 2024-02-21 DOI: 10.1007/s43684-023-00057-3

Qiufeng Yu, Hui Zhao, Li Teng, Li Li, Ansar Yasar, Stéphane Galland

{"title":"Prediction for nonlinear time series by improved deep echo state network based on reservoir states reconstruction","authors":"Qiufeng Yu, Hui Zhao, Li Teng, Li Li, Ansar Yasar, Stéphane Galland","doi":"10.1007/s43684-023-00057-3","DOIUrl":"10.1007/s43684-023-00057-3","url":null,"abstract":"<div><p>With the aim to enhance prediction accuracy for nonlinear time series, this paper put forward an improved deep Echo State Network based on reservoir states reconstruction driven by a Self-Normalizing Activation (SNA) function as the replacement for the traditional Hyperbolic tangent activation function to reduce the model’s sensitivity to hyper-parameters. The Strategy was implemented in a two-state reconstruction process by first inputting the time series data to the model separately. Once, the time data passes through the reservoirs and is activated by the SNA activation function, the new state for the reservoirs is created. The state is input to the next layer, and the concatenate states module saves. Pairs of states are selected from the activated multi-layer reservoirs and input into the state reconstruction module. Multiple input states are transformed through the state reconstruction module and finally saved to the concatenate state module. Two evaluation metrics were used to benchmark against three other ESNs with SNA activation functions to achieve better prediction accuracy.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-023-00057-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140443110","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

Shapley value: from cooperative game to explainable artificial intelligence 沙普利值：从合作博弈到可解释的人工智能

自主智能系统(英文) Pub Date : 2024-02-09 DOI: 10.1007/s43684-023-00060-8

Meng Li, Hengyang Sun, Yanjun Huang, Hong Chen

{"title":"Shapley value: from cooperative game to explainable artificial intelligence","authors":"Meng Li, Hengyang Sun, Yanjun Huang, Hong Chen","doi":"10.1007/s43684-023-00060-8","DOIUrl":"10.1007/s43684-023-00060-8","url":null,"abstract":"<div><p>With the tremendous success of machine learning (ML), concerns about their black-box nature have grown. The issue of interpretability affects trust in ML systems and raises ethical concerns such as algorithmic bias. In recent years, the feature attribution explanation method based on Shapley value has become the mainstream explainable artificial intelligence approach for explaining ML models. This paper provides a comprehensive overview of Shapley value-based attribution methods. We begin by outlining the foundational theory of Shapley value rooted in cooperative game theory and discussing its desirable properties. To enhance comprehension and aid in identifying relevant algorithms, we propose a comprehensive classification framework for existing Shapley value-based feature attribution methods from three dimensions: Shapley value type, feature replacement method, and approximation method. Furthermore, we emphasize the practical application of the Shapley value at different stages of ML model development, encompassing pre-modeling, modeling, and post-modeling phases. Finally, this work summarizes the limitations associated with the Shapley value and discusses potential directions for future research.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-023-00060-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139850285","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

Driving into the future: a cross-cutting analysis of distributed artificial intelligence, CCAM and the platform economy 驶向未来：对分布式人工智能、CCAM 和平台经济的横向分析

自主智能系统(英文) Pub Date : 2024-01-03 DOI: 10.1007/s43684-023-00059-1

Marc Guerreiro Augusto, Benjamin Acar, Andrea Carolina Soto, Fikret Sivrikaya, Sahin Albayrak

{"title":"Driving into the future: a cross-cutting analysis of distributed artificial intelligence, CCAM and the platform economy","authors":"Marc Guerreiro Augusto, Benjamin Acar, Andrea Carolina Soto, Fikret Sivrikaya, Sahin Albayrak","doi":"10.1007/s43684-023-00059-1","DOIUrl":"10.1007/s43684-023-00059-1","url":null,"abstract":"<div><p>The future of driving is autonomous. It requires a comprehensive stack of embedded software components, enabled by open-source and proprietary platforms at different abstraction layers, and then operating within a larger ecosystem. Autonomous driving demands connectivity, cooperation and automation to form the cornerstone of autonomous mobility solutions. Platform economy principles have revolutionized the way we produce, deliver and consume products and services worldwide. More and more businesses in the field of mobility and transport appear to implement transaction, innovation, and integration platforms as core enablers for Mobility-as-a-Service and transport applications. Artificial intelligence approaches, especially those dealing with distributed systems, enable new mobility solutions, such as autonomous driving. This paper contributes to understanding the intertwining role between distributed artificial intelligence, autonomous mobility and the resulting platform ecosystem. A systematic literature review is applied, in order to identify the intersection between those aspects. Furthermore, the research project BeIntelli is considered as a hands-on application of our findings. Taking into account our analysis and the aforementioned research project, we pose a blueprint architecture for autonomous mobility. This architecture is the subject of further research. Our conclusions facilitate the development and implementation of future urban transportation systems and resulting mobility ecosystems in practice.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-023-00059-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139387631","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

Distilling base-and-meta network with contrastive learning for few-shot semantic segmentation 利用对比学习提炼基元和元网络，实现少量语义分割

自主智能系统(英文) Pub Date : 2023-11-27 DOI: 10.1007/s43684-023-00058-2

Xinyue Chen, Yueyi Wang, Yingyue Xu, Miaojing Shi

{"title":"Distilling base-and-meta network with contrastive learning for few-shot semantic segmentation","authors":"Xinyue Chen, Yueyi Wang, Yingyue Xu, Miaojing Shi","doi":"10.1007/s43684-023-00058-2","DOIUrl":"10.1007/s43684-023-00058-2","url":null,"abstract":"<div><p>Current studies in few-shot semantic segmentation mostly utilize meta-learning frameworks to obtain models that can be generalized to new categories. However, these models trained on base classes with sufficient annotated samples are biased towards these base classes, which results in semantic confusion and ambiguity between base classes and new classes. A strategy is to use an additional base learner to recognize the objects of base classes and then refine the prediction results output by the meta learner. In this way, the interaction between these two learners and the way of combining results from the two learners are important. This paper proposes a new model, namely Distilling Base and Meta (DBAM) network by using self-attention mechanism and contrastive learning to enhance the few-shot segmentation performance. First, the self-attention-based ensemble module (SEM) is proposed to produce a more accurate adjustment factor for improving the fusion of two predictions of the two learners. Second, the prototype feature optimization module (PFOM) is proposed to provide an interaction between the two learners, which enhances the ability to distinguish the base classes from the target class by introducing contrastive learning loss. Extensive experiments have demonstrated that our method improves on the PASCAL-5<sup><i>i</i></sup> under 1-shot and 5-shot settings, respectively.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-023-00058-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139234399","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

Remote collaborative process optimization in research and design of industrial manufacturing 工业制造研究与设计中的远程协作流程优化

自主智能系统(英文) Pub Date : 2023-11-20 DOI: 10.1007/s43684-023-00056-4

Siqin Wang, Qingdu Li

引用次数: 0

Reviews and prospects in satellite range scheduling problem 卫星航程调度问题的回顾与展望

自主智能系统(英文) Pub Date : 2023-10-18 DOI: 10.1007/s43684-023-00054-6

Shuwei Li, Qingyun Yu, Hao Ding

{"title":"Reviews and prospects in satellite range scheduling problem","authors":"Shuwei Li, Qingyun Yu, Hao Ding","doi":"10.1007/s43684-023-00054-6","DOIUrl":"10.1007/s43684-023-00054-6","url":null,"abstract":"<div><p>With the increasing number of space satellites, the demand for satellite communication (including maneuvering, command uploading and data downloading) has also grown significantly. However, the actual communication resources of ground station are relatively limited, which leads to an oversubscribed problem. How to make use of limited ground station resources to complete satellite communication requests more fully and efficiently in the strict visible time is the focus of satellite range scheduling research. This paper reviews and looks forward to the research on Satellite Range Scheduling Problem (SRSP). Firstly, SRSP is defined as the scheduling problem of establishing communication between satellites and ground stations, and the classification and development of SRSP are introduced. Then, this paper analyzes three common problem description models, and establishes a mathematical model based on the analysis of optimization objectives and constraints. Thirdly, this paper classifies and summarizes the common solving methods of SRSP, and analyzes their characteristics and application scenarios. Finally, combined with the work in this paper, the future research direction of SRSP is envisioned.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-023-00054-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135885180","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 dynamic core evolutionary clustering algorithm based on saturated memory 基于饱和内存的动态核心进化聚类算法

自主智能系统(英文) Pub Date : 2023-10-11 DOI: 10.1007/s43684-023-00055-5

Haibin Xie, Peng Li, Zhiyong Ding

{"title":"A dynamic core evolutionary clustering algorithm based on saturated memory","authors":"Haibin Xie, Peng Li, Zhiyong Ding","doi":"10.1007/s43684-023-00055-5","DOIUrl":"10.1007/s43684-023-00055-5","url":null,"abstract":"<div><p>Because the number of clustering cores needs to be set before implementing the K-means algorithm, this type of algorithm often fails in applications with increasing data and changing distribution characteristics. This paper proposes an evolutionary algorithm DCC, which can dynamically adjust the number of clustering cores with data change. DCC algorithm uses the Gaussian function as the activation function of each core. Each clustering core can adjust its center vector and coverage based on the response to the input data and its memory state to better fit the sample clusters in the space. The DCC algorithm model can evolve from 0. After each new sample is added, the winning dynamic core can be adjusted or split by competitive learning, so that the number of clustering cores of the algorithm always maintains a better adaptation relationship with the existing data. Furthermore, because its clustering core can split, it can subdivide the densely distributed data clusters. Finally, detailed experimental results show that the evolutionary clustering algorithm DCC based on the dynamic core method has excellent clustering performance and strong robustness.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-023-00055-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136209955","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 tripartite evolutionary game analysis of providing subsidies for pick-up/drop-off strategy in carpooling problem 拼车问题中为接送策略提供补贴的三方进化博弈分析

自主智能系统(英文) Pub Date : 2023-09-25 DOI: 10.1007/s43684-023-00053-7

Zeyuan Yan, Li Li, Hui Zhao, Yazan Mualla, Ansar Yasar

{"title":"A tripartite evolutionary game analysis of providing subsidies for pick-up/drop-off strategy in carpooling problem","authors":"Zeyuan Yan, Li Li, Hui Zhao, Yazan Mualla, Ansar Yasar","doi":"10.1007/s43684-023-00053-7","DOIUrl":"10.1007/s43684-023-00053-7","url":null,"abstract":"<div><p>Although the pick-up/drop-off (PUDO) strategy in carpooling offers the convenience of short-distance walking for passengers during boarding and disembarking, there is a noticeable hesitancy among commuters to adopt this travel method, despite its numerous benefits. Here, this paper establishes a tripartite evolutionary game theory (EGT) model to verify the evolutionary stability of choosing the PUDO strategy of drivers and passengers and offering subsidies strategy of carpooling platforms in carpooling system. The model presented in this paper serves as a valuable tool for assessing the dissemination and implementation of PUDO strategy and offering subsidies strategy in carpooling applications. Subsequently, an empirical analysis is conducted to examine and compare the sensitivity of the parameters across various scenarios. The findings suggest that: firstly, providing subsidies to passengers and drivers, along with deductions for drivers through carpooling platforms, is an effective way to promote wider adoption of the PUDO strategy. Then, the decision-making process is divided into three stages: initial stage, middle stage, and mature stage. PUDO strategy progresses from initial rejection to widespread acceptance among drivers in the middle stage and, in the mature stage, both passengers and drivers tend to adopt it under carpooling platform subsidies; the factors influencing the costs of waiting and walking times, as well as the subsidies granted to passengers, are essential determinants that require careful consideration by passengers, drivers, and carpooling platforms when choosing the PUDO strategy. Our work provides valuable insight into the PUDO strategy’s applicability and the declared results provide implications for traffic managers and carpooling platforms to offer a suitable incentive.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-023-00053-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135816849","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

Robust formation control for unicycle robots with directional sensor information 具有方向传感器信息的独轮车机器人鲁棒编队控制

自主智能系统(英文) Pub Date : 2023-08-18 DOI: 10.1007/s43684-023-00052-8

Yibei Li, Lizheng Liu, Zhongxue Gan, Xiaoming Hu

引用次数: 0

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