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12th International Conference on CYBER Technology in automation, control, and Intelligent Systems, CYBER 2022

作者： Sun, Shijie Chen, Li Liang, Wei An, Haibo Zhang, Yinlong School of Artificial Intelligence Shenyang University of Technology Shenyang110020 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China Science and Technology on Information Systems Engineering Laboratory The 28th Research Institute of Cetc Jiangsu Nanjing210007 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

VSLAM shows promising potential for AGV navigation in digital workshops, thanks to its merits in low cost, easy configuration, high flexibility, and high accuracy. However, the AGV visual perception system could be easily affected by lighting changes, and body abrupt motions. The state-of-the-art visual feature matching methods may fail to fulfill the navigation tasks in presence of these challenging issues. In order to solve these problems, we propose a feature matching method based on feature space constraints and inertial assistance. In order to reduce the computational time, the spatial constraints are designed to delete the obvious false matches. Besides, the Inertia Assistance Visual Sample Consensus (IAVSAC) is proposed to reduce the computational time in discriminating the wrong feature matches. In order to ensure the reliability of the inertial data, the analysis of IMU noise has been performed. The best IMU configuration with the smallest noises is designed. Our proposed method has been evaluated on the developed AGV navigation platform. The results show that the feature matching accuracy rate and computational time of ours are more favorable than those of RANSAC and MSAC. © 2022 ieee.

关键词： Navigation

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Real-time Dynamic-consistent Motion Planning for Over-actuated UAVs

Real-time Dynamic-consistent Motion Planning for Over-actuat...

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ieee International Conference on Robotics and automation (ICRA)

作者： Yao Su Jingwen Zhang Ziyuan Jiao Hang Li Meng Wang Hangxin Liu National Key Laboratory of General Artificial Intelligence Beijing Institute for General Artificial Intelligence (BIGAI)

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Existing motion planning approaches for over-actuated unmanned aerial vehicle (UAV) platforms can achieve online planning without considering dynamics. However, in many envisioned application areas such as aerial manipulation, payload delivery, and moving target tracking, it is critical to ensure dynamic consistency in the generated trajectory. The dynamics of these platforms introduce a high nonlinearity, leading to a substantial increase in computational burden. This paper presents an efficient method to plan motions that are consistent with the dynamics of over-actuated UAVs. With a hierarchical control structure, the dimension of the optimization problem is greatly reduced with synthesized wrench commands. Additionally, by exploring the dynamics of over-actuated UAVs, the complex planning process is decoupled into two simpler sub-problems. As a result, the proposed planner can be solved as two small quadratic programmings (QPs) and deployed in real-time. The computational efficiency and dynamic consistency of the proposed method are verified through both simulations and experiments, including comparison with other approaches and dynamic target tracking.

关键词： Target tracking computational modeling Dynamics Autonomous aerial vehicles Real-time systems Planning Trajectory

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Hybrid Modeling and Switching control of Electric Vehicle Aggregation for Frequency Regulation

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ieee Transactions on Sustainable Energy 2025年

作者： Xu, Lei Dou, Chunxia Yue, Dong Zhang, Yudi Zhang, Bo Li, Houjun Bu, Xiande Nanjing University of Posts and Telecommunication College of Automation and Artificial Intelligence Nanjing210000 China Nanjing University of Posts and Telecommunications Institute of Advanced Technology Nanjing210000 China University of Bristol School of Management BristolBS8 1QU United Kingdom State Grid Smart Grid Research Institute Co. Ltd Nanjing210000 China

The aggregation and control of massive electric vehicles (EVs) are crucial for grid frequency regulation (FR). However, challenges such as disordered charging, high computational and communication burdens need to be addressed. To this end, a hierarchical hybrid modeling and switching control method for EV aggregation (EVA) is proposed. For modeling, a hybrid state set for EVs comprising three discrete states and one dynamic state is established at the local level. The dynamic state's flexibility allows EVs to charge orderly while considering user demands. At the aggregation level, a Markov-based EVA state space model is designed, integrating the user's willingnessto-pay (WTP) index and hybrid state. It estimates the EVA's FR capacity (FRC) with lower communication burden and reduces computational burden by simplifying control dimensions. For control, a model predictive control (MPC)-based state switching method is designed at the aggregation level, considering user's FR willingness and power cancellation issue. Furthermore, a predictive compensation mechanism is designed to address model parameter errors resulting from asynchronous control cycles. At the local level, a probabilistic response method is proposed for responding to dispatched control signals, which reduces battery degradation through the state of charge (SOC) based response probability generation. Simulation results validate the method's effectiveness. © 2010-2012 ieee.

关键词： State of charge

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Data Augmentation for Cardiovascular Time Series Data Using WaveNet

Data Augmentation for Cardiovascular Time Series Data Using ...

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ieee symposium Series on computational intelligence (SSCI)

作者： Robert Feldhans Alexander Schulz Johannes Kummert Moriz Habigt Maike Stemmler Christina Kohler Dirk Abel Rolf Rossaint Barbara Hammer Machine Learning Group Bielefeld University Bielefeld Germany Anaesthesiology Clinic Faculty of Medicine RWTH Aachen University Aachen Germany Institute of Automatic Control RWTH Aachen University Aachen Germany

In this work we present a novel approach for generating cardiovascular data using a modified WaveNet architecture. This can enable further research in areas where data is scarce and hard to obtain. By generating additional time series data in a set of animal tests performance of existing models could be improved and more difficult approaches, that require substantial amounts of data, attempted. We validate our approach on a classification task and compare it to similar methods of data augmentation.

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Generating Cardiovascular Data to Improve Training of Assistive Heart Devices

Generating Cardiovascular Data to Improve Training of Assist...

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ieee symposium Series on computational intelligence (SSCI)

作者： Johannes Kummert Alexander Schulz Robert Feldhans Moriz Habigt Maike Stemmler Christina Kohler Dirk Abel Rolf Rossaint Barbara Hammer Machine Learning Group Bielefeld University Bielefeld Germany Anaesthesiology Clinic Faculty of Medicine RWTH Aachen University Aachen Germany Institute of Automatic Control RWTH Aachen University Aachen Germany

In many medical applications data is a scarce resource and can often only be obtained with invasive surgery. This is for instance the case for physiological cardiovascular data that is necessary to improve the functionality of assistive heart devices. In this work we explore the viability of a GAN architecture to generate cardiovascular data towards enriching a data set obtained in animal testing on which training of future applications can be improved which potentially reduces the need for further animal testing. We evaluate the usefulness of our synthesized data using a downstream task.

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An Approach for Modeling and Analysis of Insulin Biosynthesis using Petri Nets

An Approach for Modeling and Analysis of Insulin Biosynthesi...

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ieee symposium on computational intelligence and Bioinformatics and computational Biology (CIBCB)

作者： Maria Leandra Guateque Antonio Basilio de Miranda Marcos Catanho Edward H. Haeusler Sérgio Lifschitz Departamento de Informatica PUC-Rio Rio de Janeiro Brazil Instituto Oswaldo Cruz FIOCRUZ Rio de Janeiro Brazil

ISBN: (数字)9798350356632

ISBN: (纸本)9798350356649

Insulin synthesis is a complex and essential biological process involving a series of interconnected subprocesses. We present a model of the translational process and post-translational modifications occurring in insulin biosynthesis. Using Colored Petri Nets (CPNs), we accurately represent sets of interactions among components such as amino acids, polypeptide chains, and other cellular compounds. This CPN framework captures the intricate state changes within biological processes and establishes a theoretical foundation for modeling and analyzing other critical biological processes.

关键词： Analytical models Biological system modeling computational modeling Petri nets Biological processes Process control Amino acids Regulation Insulin In vitro

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LEECAU-Net: A Latent Entropy-Quantized and Efficient Channel Attention-Aided U-Net Model for Microscopic Medical Image Segmentation 9

LEECAU-Net: A Latent Entropy-Quantized and Efficient Channel...

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9th ieee International Conference on Progress in Informatics and Computing, PIC 2024

作者： Das, Sanchita Maity, Gouranga Mandal, Diptarka Kaplun, Dmitrii Voznesensky, Alexander Sarkar, Ram Jadavpur University Dept. of Cse Kolkata India Defence Instituite of Advanced Technology Department of Applied Mathematics Pune India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China St. Petersburg Electrotechnical University 'LETI' Dept. of Automation and Control Process St. Petersburg Russia

ISBN: (纸本)9798350363203

Microscopic medical image segmentation is essential for detailed cellular-level analysis in computational pathology, offering insights into disease mechanisms. However, this task is particularly challenging due to the high similarity between different cellular classes and variability within each class, especially in densely packed or overlapping cell regions. To this end, we present LEECAU-Net (Latent Entropy-quantized and Efficient Channel Attention-aided U-Net), a U-Net-based model designed to improve segmentation accuracy through a dual-attention mechanism. Built on an Inceptionv4 backbone, LEECAU-Net's encoder integrates Latent Entropy-quantized Channel Attention (LECA) and Efficient Channel Attention (ECA) modules. The ECA module enhances the network's ability to focus on essential channel dependencies, while LECA leverages entropy quantification to prioritize channels rich in information, leading to better differentiation between similar structures. We have tested LEECAU-Net on three challenging publicly available datasets: Triple Negative Breast Cancer (TNBC), Multi-organ Nuclei Segmentation (MoNuSeg), and Cervical Nucleus Segmentation (CNS). Across these datasets, our model achieves higher performance metrics than numerous state-of-the-art approaches reported in the literature. The improved results underscore the effectiveness of LEECAU-Net's architecture in handling the complexity of microscopic medical images, making it a valuable tool for advancing automated cellular analysis in biomedical research. The source code and more results are made available at https://***/Cmatermedicalimageanalysis/LEECAU-Net. © 2024 ieee.

关键词： Image segmentation

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A Real Time Self-Generating control for AI Platforms

A Real Time Self-Generating Control for AI Platforms

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International symposium on Applied computational intelligence and Informatics ( SACI)

作者： Mircea Trifan Bogdan Ionescu Dan Ionescu School of Electrical Engineering and Computer Science University of Ottawa Ottawa Ontario Canada

ISBN: (数字)9798350329520

ISBN: (纸本)9798350329537

Recent advancements in hardware and software implementations of Artificial intelligence have sparked a multitude of revolutionary applications in the theory and implementation of AI algorithms and tools. Significant new developments have been driven by the application of the Transformer concept in the fields of Large Language Models (LLMs), Reinforcement Learning, and other areas. This in turn led to capturing long-range dependencies and contextual information based on data. More recently, strong positions in the AI research community, around the proper implementations and usage of certain Machine Learning (ML) applications, have been thoroughly debated. However, it is very much known, that ChatGPT and other like platforms, such as Llama, or large GNNs, suffer from a series of black-box drawbacks out of which the “factual accuracy”, “halucinations”, “overgeneralization” and others open loop LLMs were reported in the literature. This paper presents an Autonomic Computing (AC) closed-loop architecture that manages and gathers data from user prompts via a DOMifire module. The DOMifire acts as the sensor element of the LLM AC system, referred to as the Plant. This data is logically compared by an Expert System (ES), which serves as the core of the Autonomic Manager in the AC loop of the LLM, with the data obtained from the LLM's output―in this case, the responses generated by ChatGPT. After a reduced number of iterations, the results are evaluated using a Mean Absolute Scaled Error (MASE) metric. In the context of a time series, this process results in a stable set of sentences or rules produced by the Knowledge Base module. An example, in which the “Time Series” of AutoGluOn illustrates the AC - AI interactions for a complementary contributions to a more robust AI platform. An example of the interaction AC-AI is given in the Conclusion section of this paper.

关键词： Training automation computational modeling Architecture Time series analysis Computer architecture Chatbots

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Threat Modeling and Reasoning for Industrial control System Assets 20

Threat Modeling and Reasoning for Industrial Control System ...

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20th ieee Int symposium on Parallel and Distributed Processing with Applicat / 15th ieee Int Conf on Social Comp and Networking / 12th ieee Int Conf on Big Data and Cloud Comp / 12th ieee Int Conf on Sustainable Comp and Commun

作者： Zhang, Shugin Shi, Peiyu Du, Tianhui Su, Xinyu Han, Yunfei Chen, Peng Zhongyuan Univ Technol Coll Comp Sci Zhengzhou 450007 Peoples R China

ISBN: (纸本)9781665464970

With the evolution of the industry from automation to intelligence, the control, and management of industry have gradually become intelligent and simple, and the Industrial Internet of Things has emerged. The most important industrial control system in the industrial Internet of Things has improved the control efficiency of managers to deal with emergencies, but it also makes hackers available. To improve the response-ability of the industrial control system to attack, a threat modeling and reasoning base on industrial control system assets is proposed. The model constructs the asset structure of the industrial control system through the description of assets and the Purdue model in ATT&CK, and then establishes a threat model through attackers and the consequences of attackers. Thus, it can effectively deal with the attack of the attacker. On this basis, the asset security ontology of the industrial control system(ASOICS) is constructed and based on ASOICS, the asset security inference rules under ICS are designed. The semantic relationship between asset security of industrial control system is established by using the expression ability of ontology which can improve the response speed of security personnel to ICS attacks. Finally, this method is applied to the real industrial control system attack case scenario for analysis, and the attack and assets are mapped to the knowledge map. The potential risks and actual attack scenarios of the attacked assets are successfully detected, which proves the effectiveness of the proposed method.

关键词： Industrial control system Threat modeling Ontology construction Assets Reasoning Knowledge Graph

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Electronic Rotor Speed control for a Sustainable Aviation Fuel Pump Test Bench

Electronic Rotor Speed Control for a Sustainable Aviation Fu...

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International symposium on Applied computational intelligence and Informatics ( SACI)

作者： Károly Tamás Beneda Bendegúz Balázs Bánfi Department of Aeronautics and Naval Architecture Faculty of Transportation Engineering and Vehicle Engineering Budapest University of Technology and Economics Budapest Hungary

ISBN: (数字)9798350329520

ISBN: (纸本)9798350329537

To evaluate the combustion characteristics of sustainable aviation fuels on a research gas turbine engine, a separate test bench with a fuel pump has to be connected to the turbine. To keep the gas turbine unit connected with the pump unit and to operate the pump based on the required fuel the throughput must be determined. The pump test unit was not able to operate under static conditions since it only had an open-loop control. A closed-loop control had to be designed which operated based on a given RPM reference. A system identification was commenced on the pump unit, then a PID control was designed and implemented. All the identification and the controlling was tasked to a microcontroller. The relevant commands and given references were given to the controller via a computer connection.

关键词： Temperature measurement Viscosity Microcontrollers Current measurement Force Throughput Fuels

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