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Chinese Automation Congress (CAC)

作者： Haolin Li Peng Yi Dixiao Wei Wenyan Bai Department of Control Science and Engineering Tongji University Shanghai China Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Beijing Aerospace Automatic Control Institute Beijing China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

The problem of confrontation games between multi-agent teams has attracted considerable interest, and the question of how to ensure effective coordination of heterogeneous agents in dynamic and adversarial environments remains a significant challenge. In this paper, we present a decision-making methodology for a confrontation game between two teams, which is based on large language models (LLMs). The game is designed as a competitive Seek-and-Take task comprising three distinct robot types: reconnaissance, grabbing, and blocking robots. The LLMs enhance the robot team's decision-making efficiency, delivering high-level commands that significantly improve coordination and adversarial performance. The game is initially formalized as a partially observable Markov decision process. Subsequently, a systematic construction of the LLM agents is provided, enabling real-time decisions based on the state information of a heterogeneous robot team (HRT). Detailed prompt design and the “Zero-shot-CoT” procedure are provided, and a cue word iteration method with reflections is proposed as a means of enhancing the decision-making efficiency of the LLMs and generating optimal collaboration strategies. Finally, the efficacy of the methodology is validated through experimentation with multiple LLMs in a multi-robot simulation environment. The results demonstrate that LLMs improve the efficiency and adaptability of decision-making in dynamic environments by serving as a core decision module, generating optimal strategies through real-time analysis.

关键词： Systematics Robot kinematics Large language models Markov decision processes Decision making Collaboration Games Reconnaissance Real-time systems Reflection

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Character Recognition of Vedic Sanskrit using Deep Learning Algorithms 3

Character Recognition of Vedic Sanskrit using Deep Learning ...

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3rd IEEE International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2022

作者： Pandey, Mrinal Pandey, Rashmikiran Nazarov, Alexey Moscow Institute of Physics and Technology Department of Radio Engineering and Cybernetics Moscow Russia Federal Research Center Computer Science and Control Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9781665462006

One of the applications of deep learning is deciphering the unscripted text over the walls and pillars of historical monuments is the major source of information extraction. This information gives us an idea about the art and the culture of that respective era. In this paper we proposed a model for digital recognition of textual characters written in Sanskrit language using the 4-fold Convolutional Neural Network (CNN) architecture. At the first stage we remove the noise and subsequently segmentation of characters from the input image is performed, later we convert the image into binary image format by the help of image processing techniques. The research is performed over the Devanagari script which was developed during 1st to the 4th century CE and is common to many languages from the Indian subcontinent. © 2022 IEEE.

关键词： Character recognition

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TerrainSense: Vision-Driven Mapless Navigation for Unstructured Off-Road Environments

TerrainSense: Vision-Driven Mapless Navigation for Unstructu...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Bilal Hassan Arjun Sharma Nadya Abdel Madjid Majid Khonji Jorge Dias Khalifa University Center for Autonomous Robotic Systems (KUCARS) Abu Dhabi UAE Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi UAE

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Navigating autonomous vehicles efficiently across unstructured and off-road terrains remains a formidable challenge, often requiring intricate mapping or multi-step pipelines. However, these conventional approaches struggle to adapt to dynamic environments. This paper presents TerrainSense, an end-to-end framework that overcomes these limitations. By utilizing a transformers, TerrainSense detects lane semantics and topology from camera images, enabling mapless path planning without the reliance on highly detailed maps. The efficacy of TerrainSense was rigorously assessed on six diverse datasets, evaluating its efficacy in detection, segmentation, and path prediction using various metrics. Notably, it outperforms the other state-of-the-art methods by 9.32% in precisely predicting the path with 18.28% faster inference time.

关键词： Navigation Semantics Pipelines Transformers Robustness Path planning Topology

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Unified Temporal Erasable Itemset Mining with a Lower-Bound Strategy

Unified Temporal Erasable Itemset Mining with a Lower-Bound ...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Hong, Tzung-Pei Li, Jia-Xiang Tsai, Yu-Chuan Huang, Wei-Ming National University of Kaohsiung Department of Computer Science and Information Engineering Kaohsiung Taiwan National Sun Yat-Sen University Department of Computer Science and Engineering Kaohsiung Taiwan National University of Kaohsiung Library and Information Center Kaohsiung Taiwan China Steel Inc Department of Electrical and Control Kaohsiung Taiwan

ISBN: (纸本)9781665480451

Erasable itemset mining has been a valuable mining problem for manufacturers. It can extract less profitable materials from a product dataset and provide managers with good decision-making and a trade-off between cost and profit. However, the traditional erasable itemset mining methods seldom consider the time factor. For time-sensitive industries such as agro-processors, the time range is important in determining which materials are less profitable. Hong et al. first proposed the concept of temporal erasable itemset mining and seven lifespan options. They also proposed a unified temporal erasable (UTE) mining algorithm for getting incomplete temporal erasable itemsets. Howerever, the UTE algorithm does not satisfy the property of downward closure. In this work, we propose an improved algorithm to improve the performance of the UTE algorithm based on a lower-bound strategy and satisfying the property of downward closure. The proposed algorithm uses a hash table to store information that will be reused during the mining process to avoid scanning a dataset multiple times. The designed lower-bound strategy can preserve the downward closure property, narrowing the search space of candidate itemsets. In numerical experiments, we compare the performance using several metrics between the proposed method and the previous work. From the results of experiments, our proposed method outperforms the existing method on various metrics, such as execution time and the number of candidate erasable itemsets. © 2022 IEEE.

关键词： Data mining

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Robust Finite-Time Containment of Networked Heterogeneous Nonlinear Systems with Intermittent Measurement Only

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IEEE Transactions on Network science and engineering 2025年

作者： Tian, Biao Zhang, Hao Cui, Peiyu Wang, Zhuping Yan, Huaicheng Shanghai Key Laboratory of Wearable Robotics and Human-Machine Interaction Tongji University College of Electronic and Information Engineering Department of Control Science and Engineering Shanghai201804 China Ministry of Education Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems China East China University of Science and Technology School of Information Science and Engineering Shanghai200237 China

The robust finite-time containment problem of fully heterogeneous multiagent systems with uncertainties is often challenging, especially when only intermittent output measurement is used. To address the issue posed by the nonidentical system dynamics of multiple leaders, which makes the traditional distributed estimator form for the convex hull inapplicable, a distributed finite-time estimator is constructed for each follower to extract the system matrices and states of leaders. Then, in the output-triggering setting, a finite-time extended state observer driven by intermittent output measurement is developed to reconstruct the uncertainties and unmeasurable states of followers. Meanwhile, the non-continuous output measurement will lead to the differentiation of virtual control laws undefined. To solve this issue, a filtering compensation scheme-based finitetime controller via the backstepping technique is developed for nonlinear followers, ensuring practical finite-time stability (PFTS) of the closed-loop system. It is shown that the proposed algorithm steers each follower into the preset convex combination spanned by the positions of multiple leaders. The capability of the exploited control protocol is verified through simulations and experiments. 2013 IEEE.

关键词： Backstepping

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Finetuning Pre-trained Model with Limited Data for LiDAR-based 3D Object Detection by Bridging Domain Gaps

arXiv

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arXiv 2024年

作者： Jang, Jiyun Chang, Mincheol Park, Jongwon Kim, Jinkyu Department of Computer Science and Engineering Korea University Seoul02841 Korea Republic of Autonomous Driving Center Hyundai Motor Company R&D Division

LiDAR-based 3D object detectors have been largely utilized in various applications, including autonomous vehicles or mobile robots. However, LiDAR-based detectors often fail to adapt well to target domains with different sensor configurations (e.g., types of sensors, spatial resolution, or FOVs) and location shifts. Collecting and annotating datasets in a new setup is commonly required to reduce such gaps, but it is often expensive and time-consuming. Recent studies suggest that pre-trained backbones can be learned in a self-supervised manner with large-scale unlabeled LiDAR frames. However, despite their expressive representations, they remain challenging to generalize well without substantial amounts of data from the target domain. Thus, we propose a novel method, called Domain Adaptive Distill-Tuning (DADT), to adapt a pretrained model with limited target data (≈100 LiDAR frames), retaining its representation power and preventing it from overfitting. Specifically, we use regularizers to align object-level and context-level representations between the pre-trained and finetuned models in a teacher-student architecture. Our experiments with driving benchmarks, i.e., Waymo Open dataset and KITTI, confirm that our method effectively finetunes a pretrained model, achieving significant gains in accuracy. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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PathFormer: A Transformer-Based Framework for Vision-Centric autonomous Navigation in Off-Road Environments

PathFormer: A Transformer-Based Framework for Vision-Centric...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Bilal Hassan Nadya Abdel Madjid Fatima Kashwani Mohamad Alansari Majid Khonji Jorge Dias Khalifa University Center for Autonomous Robotic Systems (KUCARS) Abu Dhabi UAE Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi UAE

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

The efficient navigation of autonomous vehicles across rugged and unstructured terrains remains a significant challenge. Most existing research in this area emphasizes the need for complex mappings or intricate multi-step methodologies. However, these traditional approaches often struggle to adapt to dynamic changes in environmental conditions. In this paper, we introduce PathFormer, an end-to-end framework designed specifically to address these challenges. PathFormer utilizes transformers to decode free-space semantics and configurations directly from camera images, enabling efficient path planning without the reliance on detailed, pre-existing maps. The performance of PathFormer was rigorously evaluated across diverse datasets, where it demonstrated superior capabilities, outperforming other state-of-the-art methods by 3.68% in precisely segmenting free-space regions and showing a 13.65% improvement in correctly predicting traversable paths.

关键词： Visualization Image segmentation Navigation Semantics Transformers Robustness Path planning Vehicle dynamics autonomous vehicles Intelligent robots

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Machine Learning Based Intrusion Detection System in Cloud Environment 8

Machine Learning Based Intrusion Detection System in Cloud E...

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8th International Conference on Information Technology and Nanotechnology, ITNT 2022

作者： Saeed, Muhammad Salman Saurabh, Raman Bhasme, Sarang Balasaheb Nazarov, Alexey N. Moscow Institute of Physics and Technology Department of Radio Engineering and Cybernetics Moscow Russia Federal Research Center Computer Science and Control of Russian Academy of Sciences Moscow Russia

ISBN: (数字)9781665485715

ISBN: (纸本)9781665485715

Attacks and intrusions on computer networks often have different characteristics and behaviors that require professional help. The number of attacks is growing in line with the development of computer networks. In fact, expert knowledge is declining over time and should be reviewed and made available in the system, making it necessary to hire an experienced person. Cloud computing is an advancement in IT technologies that provide users with the most up-To-date, highly sought-After virtual services with high flexibility, low infrastructure costs, and minimal maintenance. Protection against network intruders is one of the most important security challenges for cloud computing, as it affects the privacy, availability, and integrity of cloud services. Because cloud computing is a shared environment, it can be vulnerable to various threats. Because building strong access systems are essential to maintaining cloud security, but it remains an obscure goal and a major challenge due to the growing number of cyber-Attacks. In this study, a machine-learning based approach was used to propose an intrusion detection system (IDS) based on a network based cloud computing model. During the previous processing phase, we used a feature selection algorithm from the CICDDoS2019 database to select features. We have used the Naive Bayes Classifier, Decision Tree Classifier, Supporting Classifier, Logistic Regression, and Random Forest Classifier, all of which are well-known categories. Simulation results when compared to these five different classifiers, random forest algorithm show improvements in overall accuracy, precision, recall and F1 score. We explain how various characteristics of machine learning techniques can be used to build efficient IDS. The goal of this research is to develop a new method based on intrusion detection systems and their varied designs for improving intrusion detection accuracy in cloud computing. © 2022 IEEE.

关键词： Decision trees

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Resource Allocation for Blockchain-Enabled Fog Computing with Deep Reinforcement Learning 12

Resource Allocation for Blockchain-Enabled Fog Computing wit...

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12th International Conference on Communication and Network Security, ICCNS 2022

作者： Zhang, Yihe Tu, Shanshan Waqas, Muhammad Yang, Yongjie Wu, Aiming Bai, Xuetao Engineering Research Center of Intelligent Perception and Autonomous Control Faculty of Information Technology Beijing University of Technology Beijing China Computer Engineering Department College of Information Technology University of Bahrain 32038 Bahrain School of Engineering Edith Cowan University Perth6027 Australia

ISBN: (纸本)9781450397520

With the developments of the Internet of Things, the demands of low latency, high bandwidth and high-performance computing has increased higher. Therefore, the distributed computing named Fog Computing has proposed to solve the problem above. Fog computing can provide lower transmission latency, faster response time and less network congestion. However, the fog devices are unable to guarantee the security and privacy of data transmission, due to they are vulnerable to attack. Blockchain technology works as a decentralized public ledger to store and share transactions. Blockchain can improve security and protect data privacy of Fog Computing. Moreover, there are still issues in the blockchain-enabled Fog Computing, the two main issues are the energy consumption and computing efficiency. Thus, in this paper, we propose an optimization framework for blockchain-enabled Fog Computing systems to optimize resource allocation. Besides, we adopt the dueling deep reinforcement learning to obtain the optimal resource allocation strategy, with dynamically selecting the fog server, offloading decision, block size. Simulation results show that the proposed framework can reduce the energy consumption and computation overhead of the system, as well as can improve the computing efficiency. © 2022 ACM.

关键词： Blockchain

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Multi-Modal Domain Adaptation Variational Autoencoder for EEG-Based Emotion Recognition

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IEEE/CAA Journal of Automatica Sinica 2022年第9期9卷 1612-1626页

作者： Yixin Wang Shuang Qiu Dan Li Changde Du Bao-Liang Lu Huiguang He the Research Center for Brain-inspired Intelligence National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of ScienceBeijing 100190 the University of Chinese Academy of Sciences Beijing 100049 the Beijing Institute of Control and Electronic Technology Beijing 100038China the School of Mathematics and Information Sciences Yantai UniversityYantai 264003China the Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of ScienceBeijingChina the Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China

Traditional electroencephalograph(EEG)-based emotion recognition requires a large number of calibration samples to build a model for a specific subject,which restricts the application of the affective brain computer interface(BCI)in *** attempt to use the multi-modal data from the past session to realize emotion recognition in the case of a small amount of calibration *** solve this problem,we propose a multimodal domain adaptive variational autoencoder(MMDA-VAE)method,which learns shared cross-domain latent representations of the multi-modal *** method builds a multi-modal variational autoencoder(MVAE)to project the data of multiple modalities into a common *** adversarial learning and cycle-consistency regularization,our method can reduce the distribution difference of each domain on the shared latent representation layer and realize the transfer of *** experiments are conducted on two public datasets,SEED and SEED-IV,and the results show the superiority of our proposed *** work can effectively improve the performance of emotion recognition with a small amount of labelled multi-modal data.

关键词： Cycle-consistency domain adaptation electroencephalograph(EEG) multi modality variational autoencoder

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