自主智能系统(英文) Pub Date : 2024-10-17 DOI: 10.1007/s43684-024-00079-5

Haibo Xiao, Baoyun Gao, Shoukang Yu, Bin Liu, Sheng Cao, Shitong Peng

{"title":"Life cycle assessment of metal powder production: a Bayesian stochastic Kriging model-based autonomous estimation","authors":"Haibo Xiao, Baoyun Gao, Shoukang Yu, Bin Liu, Sheng Cao, Shitong Peng","doi":"10.1007/s43684-024-00079-5","DOIUrl":"10.1007/s43684-024-00079-5","url":null,"abstract":"<div><p>Metal powder contributes to the environmental burdens of additive manufacturing (AM) substantially. Current life cycle assessments (LCAs) of metal powders present considerable variations of lifecycle environmental inventory due to process divergence, spatial heterogeneity, or temporal fluctuation. Most importantly, the amounts of LCA studies on metal powder are limited and primarily confined to partial material types. To this end, based on the data surveyed from a metal powder supplier, this study conducted an LCA of titanium and nickel alloy produced by electrode-inducted and vacuum-inducted melting gas atomization, respectively. Given that energy consumption dominates the environmental burden of powder production and is influenced by metal materials’ physical properties, we proposed a Bayesian stochastic Kriging model to estimate the energy consumption during the gas atomization process. This model considered the inherent uncertainties of training data and adaptively updated the parameters of interest when new environmental data on gas atomization were available. With the predicted energy use information of specific powder, the corresponding lifecycle environmental impacts can be further autonomously estimated in conjunction with the other surveyed powder production stages. Results indicated the environmental impact of titanium alloy powder is slightly higher than that of nickel alloy powder and their lifecycle carbon emissions are around 20 kg CO<sub>2</sub> equivalency. The proposed Bayesian stochastic Kriging model showed more accurate predictions of energy consumption compared with conventional Kriging and stochastic Kriging models. This study enables data imputation of energy consumption during gas atomization given the physical properties and producing technique of powder materials.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00079-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142443184","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

Leveraging multi-output modelling for CIELAB using colour difference formula towards sustainable textile dyeing 利用色差公式为 CIELAB 建立多输出模型，实现可持续纺织品染色

自主智能系统(英文) Pub Date : 2024-09-26 DOI: 10.1007/s43684-024-00076-8

Zheyuan Chen, Jian Liu, Jian Li, Mukun Yuan, Guangping Yu

{"title":"Leveraging multi-output modelling for CIELAB using colour difference formula towards sustainable textile dyeing","authors":"Zheyuan Chen, Jian Liu, Jian Li, Mukun Yuan, Guangping Yu","doi":"10.1007/s43684-024-00076-8","DOIUrl":"10.1007/s43684-024-00076-8","url":null,"abstract":"<div><p>Textile dyeing requires optimizing combinations of ingredients and process parameters to achieve target colour properties. Modelling the complex relationships between these factors and the resulting colour is challenging. In this case, a physics-informed approach for multi-output regression to model CIELAB colour values from dyeing ingredient and process inputs is proposed. Leveraging attention mechanisms and multi-task learning, the model outperforms baseline methods at predicting multiple colour outputs jointly. Specifically, the Transformer model’s attention mechanism captures the complex interactions between dyeing ingredients and process parameters, while the multi-task learning framework exploits the intrinsic correlations among the L*, a*, and b* dimensions of the CIELAB colour space. In addition, the incorporation of physical knowledge through a physics-informed loss function integrates the CMC colour difference formula. This loss function, along with the attention mechanisms, enables the model to learn the nuanced relationships between the dyeing process variables and the final colour output, thereby improving the overall prediction accuracy. This reduces trial-and-error costs and resource waste, contributing to environmental sustainability by minimizing water and energy consumption and chemical emissions.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00076-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142413919","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

Improved vision-only localization method for mobile robots in indoor environments 改进的室内环境移动机器人纯视觉定位方法

自主智能系统(英文) Pub Date : 2024-09-18 DOI: 10.1007/s43684-024-00075-9

Gang Huang, Liangzhu Lu, Yifan Zhang, Gangfu Cao, Zhe Zhou

引用次数: 0

Competing with autonomous model vehicles: a software stack for driving in smart city environments 与自动驾驶模型车竞争：智能城市环境中的驾驶软件堆栈

自主智能系统(英文) Pub Date : 2024-08-14 DOI: 10.1007/s43684-024-00074-w

Julius Bächle, Jakob Häringer, Noah Köhler, Kadir-Kaan Özer, Markus Enzweiler, Reiner Marchthaler

{"title":"Competing with autonomous model vehicles: a software stack for driving in smart city environments","authors":"Julius Bächle, Jakob Häringer, Noah Köhler, Kadir-Kaan Özer, Markus Enzweiler, Reiner Marchthaler","doi":"10.1007/s43684-024-00074-w","DOIUrl":"10.1007/s43684-024-00074-w","url":null,"abstract":"<div><p>This article introduces an open-source software stack designed for autonomous 1:10 scale model vehicles. Initially developed for the Bosch Future Mobility Challenge (BFMC) student competition, this versatile software stack is applicable to a variety of autonomous driving competitions. The stack comprises perception, planning, and control modules, each essential for precise and reliable scene understanding in complex environments such as a miniature smart city in the context of BFMC. Given the limited computing power of model vehicles and the necessity for low-latency real-time applications, the stack is implemented in C++, employs YOLO Version 5 s for environmental perception, and leverages the state-of-the-art Robot Operating System (ROS) for inter-process communication. We believe that this article and the accompanying open-source software will be a valuable resource for future teams participating in autonomous driving student competitions. Our work can serve as a foundational tool for novice teams and a reference for more experienced participants. The code and data are publicly available on GitHub.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00074-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142411651","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 method for measuring center-axis velocity of unmanned aerial vehicles through synthetic motion blur images 通过合成运动模糊图像测量无人驾驶飞行器中心轴速度的新方法

自主智能系统(英文) Pub Date : 2024-07-09 DOI: 10.1007/s43684-024-00073-x

Quanxi Zhan, Yanmin Zhou, Junrui Zhang, Chenyang Sun, Runjie Shen, Bin He

{"title":"A novel method for measuring center-axis velocity of unmanned aerial vehicles through synthetic motion blur images","authors":"Quanxi Zhan, Yanmin Zhou, Junrui Zhang, Chenyang Sun, Runjie Shen, Bin He","doi":"10.1007/s43684-024-00073-x","DOIUrl":"10.1007/s43684-024-00073-x","url":null,"abstract":"<div><p>Accurate velocity measurement of unmanned aerial vehicles (UAVs) is essential in various applications. Traditional vision-based methods rely heavily on visual features, which are often inadequate in low-light or feature-sparse environments. This study presents a novel approach to measure the axial velocity of UAVs using motion blur images captured by a UAV-mounted monocular camera. We introduce a motion blur model that synthesizes imaging from neighboring frames to enhance motion blur visibility. The synthesized blur frames are transformed into spectrograms using the Fast Fourier Transform (FFT) technique. We then apply a binarization process and the Radon transform to extract light-dark stripe spacing, which represents the motion blur length. This length is used to establish a model correlating motion blur with axial velocity, allowing precise velocity calculation. Field tests in a hydropower station penstock demonstrated an average velocity error of 0.048 m/s compared to ultra-wideband (UWB) measurements. The root-mean-square error was 0.025, with an average computational time of 42.3 ms and CPU load of 17%. These results confirm the stability and accuracy of our velocity estimation algorithm in challenging environments.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00073-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141666199","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

An uncertainty-aware domain adaptive semantic segmentation framework 不确定性感知领域自适应语义分割框架

自主智能系统(英文) Pub Date : 2024-07-08 DOI: 10.1007/s43684-024-00070-0

Huilin Yin, Pengyu Wang, Boyu Liu, Jun Yan

{"title":"An uncertainty-aware domain adaptive semantic segmentation framework","authors":"Huilin Yin, Pengyu Wang, Boyu Liu, Jun Yan","doi":"10.1007/s43684-024-00070-0","DOIUrl":"10.1007/s43684-024-00070-0","url":null,"abstract":"<div><p>Semantic segmentation is significant to realize the scene understanding of autonomous driving. Due to the lack of annotated real-world data, the technology of domain adaptation is applied so that the model is trained on the synthetic data and inferred on the real data. However, this domain gap leads to aleatoric and epistemic uncertainty. These uncertainties link to the potential safety issue of autonomous driving in normal weather and adverse weather. In this study, we explore the scientific problem that has received sparse attention previously. We postulate that the Dual Attention module can mitigate the uncertainty in the task of semantic segmentation and provide some empirical study to validate it. Furthermore, the utilization of Kullback-Leibler divergence (KL divergence) helps the estimation of aleatoric uncertainty and boosts the robustness of the segmentation model. Our empirical study on the diverse datasets of semantic segmentation demonstrates the effectiveness of our method in normal and adverse weather. Our code is available at: https://github.com/liubo629/Seg-Uncertainty-dual-attention.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00070-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141667973","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

Multiple unmanned ship coverage and exploration in complex sea areas 复杂海域的多无人船覆盖和勘探

自主智能系统(英文) Pub Date : 2024-07-05 DOI: 10.1007/s43684-024-00069-7

Feifei Chen, Qingyun Yu

{"title":"Multiple unmanned ship coverage and exploration in complex sea areas","authors":"Feifei Chen, Qingyun Yu","doi":"10.1007/s43684-024-00069-7","DOIUrl":"10.1007/s43684-024-00069-7","url":null,"abstract":"<div><p>This study addresses the complexities of maritime area information collection, particularly in challenging sea environments, by introducing a multi-agent control model for regional information gathering. Focusing on three key areas—regional coverage, collaborative exploration, and agent obstacle avoidance—we aim to establish a multi-unmanned ship coverage detection system. For regional coverage, a multi-objective optimization model considering effective area coverage and time efficiency is proposed, utilizing a heuristic simulated annealing algorithm for optimal allocation and path planning, achieving a 99.67% effective coverage rate in simulations. Collaborative exploration is tackled through a comprehensive optimization model, solved using an improved greedy strategy, resulting in a 100% static target detection and correct detection index. Agent obstacle avoidance is enhanced by a collision avoidance model and a distributed underlying collision avoidance algorithm, ensuring autonomous obstacle avoidance without communication or scheduling. Simulations confirm zero collaborative failures. This research offers practical solutions for multi-agent exploration and coverage in unknown sea areas, balancing workload and time efficiency while considering ship dynamics constraints.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00069-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141673492","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

Water-saving control system based on multiple intelligent algorithms 基于多种智能算法的节水控制系统

自主智能系统(英文) Pub Date : 2024-07-04 DOI: 10.1007/s43684-024-00068-8

Fengnian Liu, Xiang Yu, Junya Tang

引用次数: 0

A nonlinear optimal control approach for 3-DOF four-cable driven parallel robots 3-DOF 四缆驱动并联机器人的非线性优化控制方法

自主智能系统(英文) Pub Date : 2024-07-01 DOI: 10.1007/s43684-024-00066-w

G. Rigatos, M. Abbaszadeh, J. Pomares

引用次数: 0

A binary-domain recurrent-like architecture-based dynamic graph neural network 基于二元域循环结构的动态图神经网络

自主智能系统(英文) Pub Date : 2024-06-25 DOI: 10.1007/s43684-024-00067-9

Zi-chao Chen, Sui Lin

{"title":"A binary-domain recurrent-like architecture-based dynamic graph neural network","authors":"Zi-chao Chen, Sui Lin","doi":"10.1007/s43684-024-00067-9","DOIUrl":"10.1007/s43684-024-00067-9","url":null,"abstract":"<div><p>The integration of Dynamic Graph Neural Networks (DGNNs) with Smart Manufacturing is crucial as it enables real-time, adaptive analysis of complex data, leading to enhanced predictive accuracy and operational efficiency in industrial environments. To address the problem of poor combination effect and low prediction accuracy of current dynamic graph neural networks in spatial and temporal domains, and over-smoothing caused by traditional graph neural networks, a dynamic graph prediction method based on spatiotemporal binary-domain recurrent-like architecture is proposed: Binary Domain Graph Neural Network (BDGNN). The proposed model begins by utilizing a modified Graph Convolutional Network (GCN) without an activation function to extract meaningful graph topology information, ensuring non-redundant embeddings. In the temporal domain, Recurrent Neural Network (RNN) and residual systems are employed to facilitate the transfer of dynamic graph node information between learner weights, aiming to mitigate the impact of noise within the graph sequence. In the spatial domain, the AdaBoost (Adaptive Boosting) algorithm is applied to replace the traditional approach of stacking layers in a graph neural network. This allows for the utilization of multiple independent graph learners, enabling the extraction of higher-order neighborhood information and alleviating the issue of over-smoothing. The efficacy of BDGNN is evaluated through a series of experiments, with performance metrics including Mean Average Precision (MAP) and Mean Reciprocal Rank (MRR) for link prediction tasks, as well as metrics for traffic speed regression tasks across diverse test sets. Compared with other models, the better experiments results demonstrate that BDGNN model can not only better integrate the connection between time and space information, but also extract higher-order neighbor information to alleviate the over-smoothing phenomenon of the original GCN.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00067-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142413589","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

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