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Experimental Study of Machine Learning models for Prediction of Non-Stationary Non-Seasonal Time Series 1

引用

7th international Russian automation conference, RusAutoCon 2024

作者： Shustrova, M. Mukhamadiev, R. Staroverova, N. Kazan National Reseach Technological Univercity 72 Karl Marks Street Kazan420015 Russia

ISBN: (数字)9783031824944

ISBN: (纸本)9783031824937

This article is devoted to the issue of forecasting the duration of projects in an it company specializing in automation of technological and business processes. The initial data for the study are information about real projects over 10 years. The processed data set with inclusion of additional correlating characteristics is considered. This data does not have pronounced seasonal characteristics and an explicit trend sufficient for project planning, so the task of predicting the duration of work in this case is of great relevance. The study examined time series prediction models for data of a similar nature. Machine learning models were experimentally used to predict the studied data set. To improve the selection of characteristics, modern methods of optimization and search for configuration parameters were used. The experimental part of the study investigated the results of predictions obtained by the models LSTM, Auto ARIMA, Linear regression, K-averages, ARIMA, SARIMA, Prophet. The results of each model construction were analyzed on the basis of which the models were modified to achieve the necessary criteria for the task. Comparison of the results of forecasting models on the dataset under consideration revealed that LSTM neural network models are most suitable for solving the problem. The LSTM model showed an accuracy ~ 61 times that of the Prophet model based on standard error. Linear regression and K-averages also showed a satisfactory result. The information presented in this paper can be useful for forecasting modelers, data analysts and IT specialists, engineers in the field of automation and process control. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Prediction models

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Development of a toolchain for robust Sim2Real transfer of learning-based robotics applications 1st

Development of a toolchain for robust Sim2Real transfer of l...

引用

1st international conference on Applications of AI in 5G and IOT, ICAAI5GI 2024

作者： Wrede, K. Jain, V. Dehmel, M. Hartmann, N. Lange, R. Dresden Germany

ISBN: (纸本)9781032874036

The increasing complexity of industrial production demands innovative automation solutions. This paper presents a toolchain developed for the robust Sim2Real transfer of reinforcement learning-based control policies in robotic systems, addressing challenges in the inefficient manual programming of task-specific control. Our toolchain facilitates the training within a simulated environment in NVIDIA Isaac or MuJoCo and the subsequent implementation on real-world robotic hardware using ROS 2 for seamless interaction with low-level robot controllers. Several methods to ensure robust performance on real hardware are an integral part of our approach. The first real-world experiments of a dual-arm robotic assembly task demonstrate a 100% success rate, with process times closely matching those in simulations, verifying the model’s robustness to uncertainties in system dynamics and sensor noise. © 2025 the Author(s).

关键词： Robotic assembly

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Hit the Sweet Spot! Span-Level Ensemble for Large Language models 31

Hit the Sweet Spot! Span-Level Ensemble for Large Language M...

引用

31st international conference on Computational Linguistics, COLING 2025

作者： Xu, Yangyifan Chen, Jianghao Wu, Junhong Zhang, Jiajun School of Artificial Intelligence University of Chinese Academy of Sciences China Institute of Automation Chinese Academy of Sciences China Wuhan AI Research China Shanghai Artificial Intelligence Laboratory Shanghai China

ISBN: (纸本)9798891761964

Ensembling various LLMs to unlock their complementary potential and leverage their individual strengths is highly valuable. Previous studies typically focus on two main paradigms: sample-level and token-level ensembles. Sample-level ensemble methods either select or blend fully generated outputs, which hinders dynamic correction and enhancement of outputs during the generation process. On the other hand, token-level ensemble methods enable real-time correction through fine-grained ensemble at each generation step. However, the information carried by an individual token is quite limited, leading to suboptimal decisions at each step. To address these issues, we propose SWEETSPAN, a span-level ensemble method that effectively balances the need for real-time adjustments and the information required for accurate ensemble decisions. Our approach involves two key steps: First, we have each candidate model independently generate candidate spans based on the shared prefix. Second, we calculate perplexity scores to facilitate mutual evaluation among the candidate models and achieve robust span selection by filtering out unfaithful scores. To comprehensively evaluate ensemble methods, we propose a new challenging setting (ensemble models with significant performance gaps) in addition to the standard setting (ensemble the best-performing models) to assess the performance of model ensembles in more realistic scenarios. Experimental results in both standard and challenging settings across various language generation tasks demonstrate the effectiveness, robustness, and versatility of our approach compared with previous ensemble methods. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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SLAM semantic map construction based on improved YOLOv8

SLAM semantic map construction based on improved YOLOv8

引用

2024 international conference on Mechatronic Engineering and Artificial Intelligence, MEAI 2024

作者： Qin, Lijuan Tang, Xiaoyu Wang, Xiaohan School of Information Science and Engineering Shenyang Ligong University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Science Shenyang China

ISBN: (纸本)9781510689176

Utilized SLAM (Simulated Localization And Mapping) technology for simultaneous localization and map construction, as well as relevant theoretical methods based on deep learning, to construct semantic maps. Studied a visual SLAM algorithm based on depth cameras to address the current issue of ground robots not being able to obtain effective real-time semantic information in the environment. In order to further enhance the real-time modeling and environmental awareness capabilities of the SLAM system, I have improved YOLOv8 to enable more accurate semantic segmentation. Replace the feature extraction network of YOLOv8 with ConvNeXt V2 network, effectively processing non masked (visible) pixels in the input image. The hierarchical structure of its encoder also helps to capture feature information at different levels. At the same time, change the original loss function CIoU to EfficiCIoU Loss. EfficiCIoU Loss can be more refined and accurate in refining loss calculations compared to CIoU, and a more effective loss function can help the model converge faster. © 2025 SPIE.

关键词： Semantic Segmentation

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Hierarchical Table Tennis Spin Estimation Using Ball Trajectory Data

Hierarchical Table Tennis Spin Estimation Using Ball Traject...

引用

robotics, Control and automation (ICRCA), international conference on

作者： Xudong Zhou Yuxin Wang Haibo Zhang Zhiyong Sun Erkang Cheng Bo Song Institute of Intelligent Machines Hefei Institutes of Physical Science Chinese Academy of Sciences University of Science and Technology of China Hefei China Shanghai Future Mind Company Ltd Shanghai China Intelligent Machines Hefei Institutes of Physical Science Chinese Academy of Sciences University of Science and Technology of China Shanghai Future Mind Company Ltd Hefei China

ISBN: (数字)9798331544577

ISBN: (纸本)9798331544584

Accurate spin estimation is crucial for assisting table tennis robots in defeating high-level human players. Currently, most researchers use methods based on physical models or identify logos on the flying table tennis ball to estimate spin. We try a new approach, considering the use of a data-driven method to estimate spin. However, directly using such models does not produce satisfactory results, as these methods do not consider that table tennis spin estimation is a coarse-to-fine process. To address this problem, we develop a hierarchical spin estimation network to estimate spin progressively. Furthermore, we introduce a Mix Conv-Attn Block to enhance feature extraction from table tennis trajectories. This block can capture both short-term and long-term features to improve the estimation accuracy. Comparing our approach with physical-based and non-hierarchical neural network methods, experimental results show that our method achieves superior performance.

关键词： Accuracy automation Sports equipment Neural networks Estimation Feature extraction Trajectory Object recognition Robots Sports

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Point Cloud Reconstruction and Deformation Based on Point Primitives 16

Point Cloud Reconstruction and Deformation Based on Point Pr...

引用

2024 16th international conference on Graphics and Image Processing, ICGIP 2024

作者： Wang, Le Li, Xiaoli Du, Zhenlong School of Computer Science & Technology Nanjing TECH University Nanjing211816 China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

With the rapid development of 3D point cloud data acquisition technology, its application in fields such as virtual reality, augmented reality, medical imaging analysis, and robotics is becoming increasingly widespread. Although current research mainly focuses on implicit space point cloud processing, an increasing amount of evidence suggests that explicit space methods, especially those based on point data primitives, can more effectively achieve neural surface reconstruction and point cloud deformation. This study completes reconstruction by imposing constraints in point space, normal space, and image space, and use isometric constraints to construct a point cloud deformation field for precise deformation of point cloud models. Experiments on the Studio dataset and the Stanford 3D Scanning Repository demonstrated good neural surface reconstruction effects. Compared to the Instant NGP method, the reconstruction loss was reduced by 13.9% and 27.7%, respectively, and effectively avoided reconstruction loopholes. Ablation experiments further confirmed the role of AIAP constraints in improving the quality of point cloud deformation, with optimization effects reaching 7.4%, respectively. The point cloud reconstruction and deformation method based on point primitives proposed in this paper, from explicit semantic space constraints and guided learning processes, not only completes the reconstruction and deformation of point clouds with high fidelity but also shows strong robustness and generalization. © 2025 SPIE.

关键词： Medical robotics

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Extracting Sparse Specialist models from Generalist models

Extracting Sparse Specialist Models from Generalist Models

引用

2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Yu, Tao Zhao, Xu An, Yongqi Zhu, Guibo Tang, Ming Wang, Jinqiao Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Wuhan AI Research Wuhan China

ISBN: (纸本)9798350368741

Recently, several generalist models such as Contrastive Language Image Pre-training (CLIP) have demonstrated their capabilities of performing diverse downstream tasks through zero-shot or few-shot guidance. When these generalist models are used for the specific downstream task where only a fraction of features is relevant, they would suffer from a significant redundancy of parameters. While existing methods aim to achieve sparsity and specialization, they often require additional training and large datasets. In this paper, we propose a novel framework to extract a sparse specialist model from a generalist model using only few-shot samples, without any training. Our task-specific pruning framework defines task relevance metrics and employs weighted layer-wise pruning, preserving relevant features while removing redundancies. Experiments show that our method maintains nearly identical zero-shot accuracy compared to the original generalist models at 30% sparsity, with only minimal decline at 50%. © 2025 IEEE.

关键词： Network Compression Network Pruning Vision-Language models

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Transforming Classification with Federated Learning on Blockchain: A Unique Model Integration Approach

Transforming Classification with Federated Learning on Block...

引用

2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Hao, Zhihao Zhang, Bob Li, Haisheng School of Computer Science and Artificial Intelligence Beijing Technology and Business University Beijing China Dept. of Computer and Information Science Faculty of Science and Technology University of Macau Taipa China Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation University of Macau Taipa China

ISBN: (纸本)9798350368741

The need for robust machine learning models is particularly evident in the realm of biological pattern recognition. Traditional centralized methods often struggle, as they frequently depend on large datasets that are challenging to gather due to stringent data privacy regulations. To address these limitations while maintaining classification accuracy, we propose an innovative approach that unifies models with diverse architectures within a federated learning framework built on a blockchain network. This decentralized and trustworthy system fosters effective collaboration among various models. Furthermore, we have implemented a weight distribution mechanism designed to maximize the individual strengths of each model. By leveraging blockchains inherent transparency and auditability, this approach also ensures secure and traceable data exchanges among participants. Additionally, the adaptability of the framework allows it to be extended to other domains where privacy-preserving data sharing is critical. Our experimental results showcase that the proposed methodology significantly enhances performance in classification tasks compared to existing alternatives. © 2025 IEEE.

关键词： blockchain collaborative training Federated Learning heterogeneous models

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Measuring Fairness in AI Explanations with LEADR: Local Explanation Amplification Disparity Ratio 22nd

Measuring Fairness in AI Explanations with LEADR: Local Ex...

引用

22nd international conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2024

作者： Shoeibi, Niloufar DeGange, Jonathan Shoeibi, Nastaran Shoeibi, Ali Department of Computer Science and Automation Faculty of Sciences Universidad de Salamanca Salamanca Spain LLP Madrid Spain LLP Washington United States Mazandaran University of Science and Technology Babol Iran

ISBN: (纸本)9783031704147

We investigate the fairness of local explanations in AI models by comparing the mean explanations for privileged and unprivileged groups across various datasets and model types. Specifically, we train linear, multi-layer perceptron, and explainable boosting machine (EBM) models, on several key academic datasets. Local explanations are generated using both post-hoc methods (LIME), and direct methods - logistic regression, integrated gradients and EBM local feature importance. By comparing these explanations across models and methods, we introduce a new metric, the Local Explanation Amplification Disparity Ratio (LEADR), to measure disparities in feature attribution between privileged and unprivileged groups. Our preliminary findings suggest that transparent Whitebox models may exhibit a tendency to display greater disparity in bias than opaque Blackbox models. This insight encourages further research into bias mitigation strategies that are tailored to different algorithm types, with the goal of minimizing undesired bias in AI systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Logistic regression

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Advanced Structural Design for Humanoid Robot Upper Limbs

Advanced Structural Design for Humanoid Robot Upper Limbs

引用

robotics, Control and automation (ICRCA), international conference on

作者： Yifan Liu Hongwu Wang Dianzheng Wang Yunji Li Xiangkun Zhang Robotics Engineering Center The 21st Research Institute of CETC Shanghai China

ISBN: (数字)9798331544577

ISBN: (纸本)9798331544584

In this paper, the humanoid robot is taken as the object to study the influence of the design parameters of the dual-arm base on the workspace in the logistics handling scene. First, the humanoid robot and the handling target object are modeled, the influence of the dual-arm configuration and the base constraint conditions on the dual-arm workspace is quantitatively analyzed, the mathematical relationship between the variables is established, and the parameters of the dual-arm base are optimized to achieve the best workspace. Finally, the optimization effect is verified by experiments. The results show that the method can effectively improve the cooperative workspace of the two arms and provide ideas for the design of the base.

关键词： automation Humanoid robots Arms Manipulators Stability analysis Mathematical models MATLAB Collision avoidance Optimization Logistics

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