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8th International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2023

作者： Wang, Baoji Li, Boyan Wang, Qixuan Dong, Lei Gree Altairnano New Energy INC Zhuhai519000 China School of Automation Beijing Institute of Technology Beijing China Tangshan Research Institute of Technology Tangshan China School of Software Engineering Beijing University of Technology Beijing100124 China

ISBN: (纸本)9789819975891

Lithium-ion batteries will undergo continuous aging during the process of charging and discharging. Charging and discharging cycle conditions for lithium-ion batteries are usually an important method to detect the degree of aging of lithium-ion batteries. However, the general aging cycle lasts for thousands of cycles, which is more time consuming. The charge/discharge capacity of the original condition and the accelerated condition are guaranteed to be the same, and the aging path is essentially the same way. The accelerated life test of lithium-ion battery is realized by the constant current rate accelerated operating condition design and the variable current rate accelerated operating condition design with two different constraints, and the accelerated operating condition with the minimum difference between the battery aging path of the original operating condition before acceleration is obtained by the optimization method. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Lithium-ion batteries

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5G Indoor Positioning for Manufacturing using Convolutional Neural Networks

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Procedia CIRP 2023年 120卷 1191-1196页

作者： Hannes Vietz Hamza Ben Haj Ammar Sebastian Baum Nasser Jazdi Michael Weyrich University of Stuttgart Institute of Industrial Automation and Software Engineering Pfaffenwaldring 47 70569 Stuttgart Germany

Current disrupting trends in manufacturing like mobile robot-based intralogistics and digital twins require precise indoor positioning. 5G-based positioning has the potential to be more cost-efficient than other positioning systems due to shared hardware for positioning and communication. But first it has to achieve high positioning accuracy and stability. No positioning technique has yet been established as standard in the industry in combination with 5G because traditional positioning approaches can deliver unstable results when multi-path fading, reflections, or missing line-of-sight effects occur. Identifying promising techniques is still an open research topic. It is established in the literature that deep learning-based approaches can mitigate these effects better than traditional ones by learning unique fingerprints that correspond to precise positions. However, fully-connected neural networks tend to overtrain on these fingerprints which again leads to unstable positioning due to a lack of generalization. This can be explained by the inability of fully connected neural networks to learn structural interrelationships in high dimensional data. This is because their input data is flattened, which means the structure is removed. Convolutional neural networks are being widely used with image data and can learn structural interrelationships. This paper presents a technique that uses convolutional neural networks in combination with a channel-smoothing approach to position mobile robots in the research factory ARENA 2036. The results presented in this paper indicate an indoor positioning accuracy similar to other industrial positioning systems, with improved stability compared to a fully-connected approach.

关键词： indoor positioning mobile robots deep learning 5G intralogistics

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CircRNA-disease inference using deep ensemble model based on triple association

CircRNA-disease inference using deep ensemble model based on...

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2022 Chinese automation Congress, CAC 2022

作者： Fu, Laiyi Du, Hongkai Wang, Ying Peng, Qinke Xi'an Jiaotong University School of Automation Science and Engineering Systems Engineering Institute Xi'an China Xi'an Jiaotong University School of Software Engineering Systems Engineering Institute Xi'an China

ISBN: (纸本)9781665465335

Accumulating evidence indicates more and more circular RNAs (i.e. circRNAs) have played a vital role in regulating gene expression and are related to diseases through different biological procedures. Predicting circRNA-disease associations helps to conjecture possible disease related circRNA and facilitate human disease diagnosis and downstream treatment. Nevertheless, little effort was made to uncover the interaction between various diseases and circRNAs. In our work, human circRNA-disease association network is first generated using known miRNA-circRNA interactions and disease related miRNA (microRNA) information. Then we further integrated this information to compute similarity scores between human diseases and circRNAs. Here, we proposed one deep ensemble model called DeepInteract, which first used two stacked auto-encoders to explore hidden features utilizing similarity information, and adopted a 3-layer neuron network to predict the final association. Our method is capable of capturing more complex non-linear features comparing to other approaches. Our results indicate the proposed method is superior to other previous competitors. Many prediction results have been validated by some biological experiments using our model. © 2022 IEEE.

关键词： Association reactions

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Low PAPR FBMC-OQAM System Based on Data Mapping and DFT Spreading

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IEEE Internet of Things Journal 2025年第12期12卷 22311-22324页

作者： Li, Liming Zhang, Jiliang Ding, Liqin Wang, Yang Zhang, Haining NingboTech University School of Information Science and Engineering Ningbo315100 China Harbin Institute of Technology Department of Electronic and Information Engineering Shenzhen518055 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries College of Information Science and Engineering Shenyang110819 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Nankai University College of Software Tianjin300350 China

Filter bank multicarrier with offset quadrature amplitude modulation (FBMC-OQAM) is considered as a promising waveform for Internet of Things (IoT) due to its capability to mitigate interference among asynchronous users. However, as a multicarrier technique, the FBMC-OQAM signal also has a high peak-to-average power ratio (PAPR), which is detrimental to low-power transmission. To address this issue, this paper proposes a novel discrete Fourier transform (DFT)-spread scheme to exhaustively reduce the PAPR of the FBMC-OQAM signal. In this scheme, the transmitting data symbols are first mapped with a conjugate symmetry rule and then coded by the DFT. Using this method, the preprocessing step in FBMC-OQAM that involves separating the real and imaginary parts of the quadrature amplitude modulation (QAM) symbols can be avoided. Compared with existing DFT-spread FBMC-OQAM schemes, the proposed scheme demonstrates better PAPR performance while maintaining equivalent spectral efficiency. Additionally, the computational complexity of the proposed scheme experiences only a slight increase, thereby preserving a relatively low computational burden. Numerical simulation results validate the effectiveness of the proposed scheme. Furthermore, the effect of the prototype filter on the PAPR is investigated, and a trade-off exists between the PAPR and out-of-band performance. © 2014 IEEE.

关键词： Quadrature amplitude modulation

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Multi-feature fusion friend recommendation algorithm based on complex network

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International Journal of Information and Communication Technology 2023年第4期23卷 401-423页

作者： Pan, Kan Chen, Hailong Liu, Qian Wang, Jian Pu, Yingming Yin, Chunlin Yang, Zheng Zhao, Na Electric Power Research Institute Yunnan Power Grid Co. Ltd. Kunming650217 China School of Software Yunnan University Kunming650504 China College of Information Engineering and Automation Kunming University of Science and Technology Kunming650504 China

At present, one of the problems of friend recommendation algorithms used in most social networks is that these networks often rely on a single index for recommendation. To solve this problem, multi-feature fusion (MFF) algorithm, a social network friend recommendation algorithm based on complex network theory, is proposed. The recommendation algorithm works by firstly divides the existing social networks into different communities. The importance of nodes in a social network is then calculated through the fusion of nodes’ importance information. Lastly, by integrating node importance information, friend number information and the shortest path information features are comprehensively evaluated, so as to generate final friend recommendation list. Simulation shows that with the increase of network nodes, the MFF algorithm outperforms common friend (CF) algorithm and friend similarity (FS) algorithm over all evaluation indicators including P-value, R-value and F1-value. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Complex networks

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Parallel intelligent education with ChatGPT

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Frontiers of Information Technology & Electronic engineering 2024年第1期25卷 12-18页

作者： Jiacun WANG Ying TANG Ryan HARE Fei-Yue WANG Department of Computer Science and Software Engineering Monmouth UniversityNew Jersey 07764USA Department of Electrical and Computer Engineering Rowan UniversityNew Jersey 08028USA Institute of Automation Chinese Academy of SciencesBeijing 100190China

This paper presents a framework for parallel intelligent education that involves physical and virtual learning for a personalized learning *** especially focus on Chat Generative Pre-trained Transformer(ChatGPT)owing to its considerable potential to supplement regular class *** address the strengths and weaknesses of learning with ***,we discuss the challenges and solutions of the proposed parallel intelligent education with ChatGPT.

关键词： education GPT intelligent

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Case Study: ROS-Based Fault Injection for Risk Analysis of Robotic Manipulator

Case Study: ROS-Based Fault Injection for Risk Analysis of R...

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IEEE International Conference on automation Science and engineering (CASE)

作者： Yuliang Ma Philipp Grimmeisen Andrey Morozov Institute of Industrial Automation and Software Engineering University of Stuttgart Germany

Industrial cyber-physical systems (ICPS) are becoming more complex due to increasing behavioral and structural complexity. This increases the likelihood of faults, errors, and failures. This can lead to economic losses and even hazardous events. Fault injection is an efficient method to estimate the potential risk of safety-critical ICPS. In this paper, we propose a new fault injection-based risk analysis method for Robot Operating System (ROS) and demonstrate its applicability with a robot manipulator case study. We conducted extensive fault injection experiments using a pick-and-place task. We injected two types of sensor signal faults: bias and noise. First, fault injections were implemented on a ROS/Gazebo model of the manipulator with randomly selected fault parameters such as fault type, location, magnitude, and duration. The experiments helped to identify potential failure scenarios and to find critical fault locations. The most important factor contributing to system failures was the operational phase during which the faults were injected. We then tested our fault injection method on a real Franka Emika Panda collaborative manipulator to validate the effectiveness of the proposed ROS-based fault injection method. We observed that the digital model showed similar behavior to the real manipulator.

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Parameter Optimization via Reinforcement Learning for the Regulation of Swarms 9

Parameter Optimization via Reinforcement Learning for the Re...

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9th International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2023

作者： Wu, Qizhen Liu, Gaoxiang Liu, Kexin Chen, Lei School of Automation Science and Electrical Engineering Beihang University Beijing China Environment School of Automation Science and Electrical Engineering Beihang University The State Key Laboratory of Software Development Beijing China Advanced Research Institute of Multidisciplinary Sciences Beijing Institute of Technology Beijing China Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing China

ISBN: (纸本)9798350342239

The bird-oid object (Boids) model proposes a control algorithm to make the positions between agents achieve cooperative stability. By changing the parameters of cohesion and repulsion in the algorithm, the agents in the swarm can be made to converge to different positions, causing expansion and contraction of the formation. But it is often more difficult to select the appropriate parameters to form the ideal formation. Therefore, this paper proposes a method to improve the cohesive and repulsive parameters in the Boids model based on Q-learning network to achieve a simulation scenario with continuous obstacle avoidance and maximum coverage of space. © 2023 IEEE.

关键词： Reinforcement learning

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Video Self-Supervised Cross-Pathway Training Based on Slow and Fast Pathways

Video Self-Supervised Cross-Pathway Training Based on Slow a...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Li, Jie Yang, Jing Jiang, Zhou Chen, Yuehai Du, Shaoyi School of Automation Science and Engineering Xi'an Jiaotong University Xi'an China School of Software Engineering Xi'an Jiaotong University Xi'an China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China

ISBN: (纸本)9798350337020

In the field of video self-supervised learning, contrastive instance learning methods suffer from a lack of semantic information, resulting in inadequate generalization in downstream tasks. Although optical flow can provide some semantic information, it requires significant computational cost prior to training. To address this, we propose a Video self-supervised Cross-pathway training model based on Slow and Fast pathways (VCSF). This model separately extracts temporal and spatial features from pure RGB video frames, and uses the complementary representations of the two pathways to conduct cross-pathway training. Additionally, we propose a motion perception module in the low-frame-rate space to enhance the network's ability to perceive rapidly changing human motion. We conducted extensive experiments in downstream missions of UCF101 and HMDB51, and obtained state-of-the-art results in models using the UCF101 data set for self-supervised pre-training, including motion recognition and nearest neighbor retrieval. © 2023 IEEE.

关键词： Cross-pathway training Self-supervised learning Slow and Fast pathways

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Multi-View Dynamic Kernelized Evidential Clustering

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IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2435-2450页

作者： Jinyi Xu Zuowei Zhang Ze Lin Yixiang Chen Weiping Ding the Software Engineering Institute East China Normal UniversityShanghai 200062 Shanghai Key Laboratory of Trustworthy Computing East China Normal UniversityShanghai 200062China IEEE the School of Automation Northwestern Polytechnical UniversityXi’an 710072China the School of Artificial Intelligence and Computer Science Nantong UniversityNantong 226019 the Faculty of Data Science City University of MacaoMacao 999078China

It is challenging to cluster multi-view data in which the clusters have overlapping *** multi-view clustering methods often misclassify the indistinguishable objects in overlapping areas by forcing them into single clusters,increasing clustering *** solution,the multi-view dynamic kernelized evidential clustering method(MvDKE),addresses this by assigning these objects to meta-clusters,a union of several related singleton clusters,effectively capturing the local imprecision in overlapping *** offers two main advantages:firstly,it significantly reduces computational complexity through a dynamic framework for evidential clustering,and secondly,it adeptly handles non-spherical data using kernel techniques within its objective *** on various datasets confirm MvDKE's superior ability to accurately characterize the local imprecision in multi-view non-spherical data,achieving better efficiency and outperforming existing methods in overall performance.

关键词： Evidential clustering imprecision characterizing kernel technique multi-view clustering

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