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IEEE International Conference on Industrial Informatics (INDIN)

作者： I. Seilonen T. Pirttioja P. Appelqvist K. Koskinen A. Halme Information and Computer Systems in Automation Helsinki University of Technology Espoo Finland Automation Technology Laboratory Helsinki University of Technology Espoo Finland

An approach to modeling cooperative control systems and their operation in process automation is presented in this paper. In this approach agent-oriented software engineering techniques are used throughout the design of cooperative control systems both as modeling method and as implementation technology. A part of a process automation system is modeled as a multi-agent society, which adhere to goal-oriented behavior both in their internal and cooperative operations. Another idea in the approach is to use qualitative process modeling in planning of control operations. The approach outlines a generic process automation agent system, which can be applied to building cooperative control applications. Sequential and supervisory control are presented as example applications. The approach is illustrated with examples from a prototype control system build for a laboratory test process

关键词： Automatic control Process control automation Multiagent systems Control system synthesis Control systems Software engineering Process planning Supervisory control Prototypes

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Distributed planning agents for intelligent process automation

Distributed planning agents for intelligent process automati...

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IEEE International Symposium on Computational Intelligence in Robotics and automation (CIRA)

作者： I. Seilonen T. Pirttioja P. Appelqvist A. Halme K. Koskinen Information and Computer Systems in Automation Helsinki University of Technology Espoo Finland Automation Technology Laboratory Helsinki University of Technology Espoo Finland

An approach to intelligent process automation based on distributed planning agents is presented in this paper. According to this approach a higher-level agent-based automation layer supervises an ordinary process automation system. The purpose of the agent layer is to monitor the operation of the lower-level automation system and semi-autonomously reconfigure its control logic when needed. The agent layer operates as a distributed planning and plan execution system, which creates and runs reconfiguration sequences on the ordinary automation system in order to adapt it to various situations. In this way the approach aims to increase the operational flexibility of the whole automation system. The approach is demonstrated with a laboratory test process where process startup scenarios have been imitated. Experiences from the initial test runs are also described.

关键词： Process planning Intelligent agent automation Automatic control Artificial intelligence Laboratories Testing Process control Distributed computing World Wide Web

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Robust image modeling technique with a bioluminesence image segmentation application

Robust image modeling technique with a bioluminesence image ...

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Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging

作者： Zhong, Jianghong Wang, Ruiping Tian, Jie Department of Biomedical Engineering School of Computer and Information Technology Beijing Jiaotong University Beijing 100044 China Medical Image Processing Group Key Laboratory of Complex Systems and IntelligenceScience Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9780819475138

A robust pattern classifier algorithm for the variable symmetric plane model, where the driving noise is a mixture of a Gaussian and an outlier process, is developed. The veracity and high-speed performance of the pattern recognition algorithm is proved. Bioluminescence tomography (BLT) has recently gained wide acceptance in the field of in vivo small animal molecular imaging. So that it is very important for BLT to how to acquire the highprecision region of interest in a bioluminescence image (BLI) in order to decrease loss of the customers because of inaccuracy in quantitative analysis. An algorithm in the mode is developed to improve operation speed, which estimates parameters and original image intensity simultaneously from the noise corrupted image derived from the BLT optical hardware system. The focus pixel value is obtained from the symmetric plane according to a more realistic assumption for the noise sequence in the restored image. The size of neighborhood is adaptive and small. What's more, the classifier function is base on the statistic features. If the qualifications for the classifier are satisfied, the focus pixel intensity is setup as the largest value in the ***, it will be ***,pseudo-color is added up to the result of the bioluminescence segmented image. The whole process has been implemented in our 2D BLT optical system platform and the model is proved. © 2009 SPIE.

关键词： Bioluminescence

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Classification with the hybrid of manifold learning and gabor wavelet

Classification with the hybrid of manifold learning and gabo...

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3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks

作者： Zhang, Junping Shen, Chao Feng, Jufu Shanghai Key Laboratory of Intelligent Information Processing Department of Computer Science and Engineering Fudan University Shanghai 200433 China Key Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy of Sciences Beijing 100080 China National Key Laboratory for Machine Perception School of Electronics Engineering and Computer Science Peking University Beijing 100871 China

ISBN: (纸本)354034439X

While manifold learning algorithms can discover intrinsic low-dimensional manifold embedded in the high-dimensional Euclidean space, the discriminant ability of the low-dimensional subspaces obtained by the algorithms is often lower than those obtained by the conventional dimensionality reduction approaches. Furthermore, the original feature vectors may include redundancy such as high-order correlation which cannot be removed by manifold learning algorithms. To address the two problems, we first employ Gabor wavelet to remove intrinsic redundancies of images and obtain a set of over-completed feature vectors. Then a supervised manifold learning algorithm (ULLELDA) is applied to project Gabor-based data and out-of-the-samples into a common low-dimensional subspace. Experiments in two FERET face databases indicate that Gabors indeed help supervised manifold learning to remarkably improve the discriminant ability of low-dimensional subspaces. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Wavelet transforms

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Typical curve with G1 constraints for curve completion

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Visual Computing for Industry,Biomedicine,and Art 2021年第1期4卷 283-294页

作者： Chuan He Gang Zhao Aizeng Wang Fei Hou Zhanchuan Cai Shaolin Li School of Mechanical Engineering and Automation Beihang UniversityBeijing 100191China State Key Laboratory of Lunar and Planetary Sciences Macao University of Science and TechnologyMacao 999078China State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijing 100191China State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China State Key Laboratory of Computer Science Institute of SoftwareUniversity of Chinese Academy of SciencesBeijing 100049China Faculty of Information Technology Macao University of Science and TechnologyMacao 999078China

This paper presents a novel algorithm for planar G1 interpolation using typical curves with monotonic *** G1 interpolation problem is converted into a system of nonlinear equations and sufficient conditions are provided to check whether there is a *** proposed algorithm was applied to a curve completion *** main advantages of the proposed method are its simple construction,compatibility with NURBS,and monotonic curvature.

关键词： Typical curves Monotonic curvature G1 interpolation Curve completion Euler spiral

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UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services: A Multi-Agent Deep Reinforcement Learning Approach

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 430-445页

作者： Jiawen Kang Junlong Chen Minrui Xu Zehui Xiong Yutao Jiao Luchao Han Dusit Niyato Yongju Tong Shengli Xie IEEE School of Automation Guangdong University of TechnologyGuangzhou 510006China 111 Center for Intelligent Batch Manufacturing based on IoT Technology Guangzhou 510006China Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of EducationGuangzhou 510006China School of Computer Science and Engineering Nanyang Technological UniversitySingaporeSingapore Pillar of Information Systems Technology and Design Singapore University of Technology and DesignSingaporeSingapore College of Communication Engineering Army Engineering University of PLANanjing 210007China National Natural Science Foundation of China Beijing 100085China Key Laboratory of Intelligent Detection and IoT in Manufacturing Ministry of EducationGuangzhou 510006China Guangdong Key Laboratory of IoT Information Technology Guangzhou 510006China

Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation,which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization(MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers(e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach

关键词： Avatar blockchain metaverses multi-agent deep reinforcement learning transformer UAVs

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Context Convolution Dehazing Network With Channel Attention 5

Context Convolution Dehazing Network With Channel Attention

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5th Asian Conference on Artificial Intelligence Technology, ACAIT 2021

作者： Li, Yuanyuan Meng, Jun Zhu, Zhiqin Huang, Xinghua Qi, Guanqiu Luo, Yaqin Chongqing University of Posts and Telecommunications Chongqin Key Laboratory of Image Cognition Chongqing400065 China Chongqing University College of Automation Chongqing400044 China Buffalo State College Computer Information Systems Department BuffaloNY14222 United States

ISBN: (纸本)9781665426305

Fog and haze weather conditions lead to deterioration of image visual quality. Under these conditions, advanced image processing tasks such as object detection and image segmentation are difficult to perform. To solve related problems, in this paper we propose an end-to-end context dilated convolution dehazing network with channel attention to return to a clear image from a haze image. This model uses context dilated module extracts the multi-scale scene information in the haze image, which can better maintain the original color of the image while removing the haze. The channel attention enables the model to separate the importance of features and boosts the model's power to adapt to different input scenarios. In the training phase, the model uses contrastive learning to distinguish the potential difference between the haze picture and the clear picture, helping the model to better renewal the clear image. The results of contrastive tests with existing methods indicate the proposed method has excellent dehazing performance. © 2021 IEEE.

关键词： Convolution

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NutFrame: Frame-based Conceptual Structure Induction with LLMs 30

NutFrame: Frame-based Conceptual Structure Induction with LL...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Guo, Shaoru Chen, Yubo Liu, Kang Li, Ru Zhao, Jun School of Computer and Information Technology Shanxi University Taiyuan China The Laboratory of Cognition and Decision Intelligence for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Shanghai Artificial Intelligence Laboratory Shanghai China

ISBN: (纸本)9782493814104

Conceptual structure is fundamental to human cognition and natural language understanding. It is significant to explore whether Large Language Models (LLMs) understand such knowledge. Since FrameNet serves as a well-defined conceptual structure knowledge resource, with meaningful frames, fine-grained frame elements, and rich frame relations, we construct a benchmark for coNceptual structure induction based on FrameNet, called NutFrame. It contains three sub-tasks: Frame Induction, Frame Element Induction, and Frame Relation Induction. In addition, we utilize prompts to induce conceptual structure of Framenet with LLMs. Furthermore, we conduct extensive experiments on NutFrame to evaluate various widely-used LLMs. Experimental results demonstrate that FrameNet induction remains a challenge for LLMs. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Frame Element Induction Frame Induction Frame Relation Induction FrameNet

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Low-rank Adaptation Method for Wav2vec2-based Fake Audio Detection

Low-rank Adaptation Method for Wav2vec2-based Fake Audio Det...

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2023 Workshop on Deepfake Audio Detection and Analysis, DADA 2023

作者： Wang, Chenglong Yi, Jiangyan Zhang, Xiaohui Tao, Jianhua Yan, Xinrui Xu, Le Fu, Ruibo University of Science and Technology of China Hefei China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijng China School of Artificial Intelligence University of Chinese Academy of Sciences China School of Computer and Information Technology Beijing Jiaotong University China Department of Automation Tsinghua University China

Self-supervised speech models are a rapidly developing research topic in fake audio detection. Many pre-trained models can serve as feature extractors, learning richer and higher-level speech features. However,when fine-tuning pre-trained models, there is often a challenge of excessively long training times and high memory consumption, and complete fine-tuning is also very expensive. To alleviate this problem, we apply low-rank adaptation(LoRA) to the wav2vec2 model, freezing the pre-trained model weights and injecting a trainable rank-decomposition matrix into each layer of the transformer architecture, greatly reducing the number of trainable parameters for downstream tasks. Compared with fine-tuning with Adam on the wav2vec2 model containing 317M training parameters, LoRA achieved similar performance by reducing the number of trainable parameters by 198 times. © 2023 CEUR-WS. All rights reserved.

关键词： Fake detection

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An intelligent system for Aerial image Retrieval and Classification

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3rd Hellenic Conference on Artificial Intelligence, SETN 2004

作者： Gasteratos, Antonios Zafeiridis, Panagiotis Andreadis, Ioannis Laboratory of Robotics and Automation Section of Production Systems Department of Production and Management Engineering Democritus University of Thrace Building of University’s Library Kimmeria XanthiGR-671 00 Greece Laboratory of Electronics Section of Electronics and Information Systems Technology Department of Electrical and Computer Engineering Democritus University of Thrace Vassilisis Sophias 12 XanthiGR-671 00 Greece

ISBN: (纸本)3540219374

Content based image retrieval is an active research area of pattern recognition. A new method of extracting global texture energy descriptors is proposed and it is combined with features describing the color aspect of texture, suitable for image retrieval. The same features are also used for image classification, by its semantic content. An exemplar fuzzy system for aerial image retrieval and classification is proposed. The fuzzy system calculates the degree that a class, such as sea, clouds, desert, forests and plantations, participates in the input image. Target applications include remote sensing, computer vision, forestry, fishery, agricultures, oceanography and weather forecasting. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Fuzzy systems

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