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A Crowd Equivalence-Based Massive Member Model Generation Method for Crowd Science Simulations

International Journal of Crowd Science 2022年第1期6卷 23-33页

作者： Xing, Aoqiang Sun, Hongbo School of Computer and Control Engineering Yantai University Yantai264005 China

Crowd phenomena are widespread in human society, but they cannot be observed easily in the real world, and research on them cannot follow traditional ways. Simulation is one of the most effective means to support studies about crowd phenomena. As modelbased scientific activities, crowd science simulations take extra efforts on member models, which reflect individuals who own characteristics such as heterogeneity, large scale, and multiplicate connections. Unfortunately, collecting enormous members is difficult in reality. How to generate tremendous crowd equivalent member models according to real members is an urgent problem to be solved. A crowd equivalence-based massive member model generation method is proposed. Member model generation is accomplished according to the following steps. The first step is the member metamodel definition, which provides patterns and member model data elements for member model definition. The second step is member model definition, which defines types, quantities, and attributes of member models for member model generation. The third step is crowd network definition and generation, which defines and generates an equivalent large-scale crowd network according to the numerical characteristics of existing networks. On the basis of the structure of the large-scale crowd network, connections among member models are well established and regarded as social relationships among real members. The last step is member model generation. Based on the previous steps, it generates types, attributes, and connections among member models. According to the quality-time model of crowd intelligence level measurement, a crowd-oriented equivalence for crowd networks is derived on the basis of numerical characteristics. A massive member model generation tool is developed according to the proposed method. The member models generated by this tool possess multiplicate connections and attributes, which satisfy the requirements of crowd science simulations well. T

关键词： Data models Numerical models Level measurement Task analysis Standards

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Leveraging Data Fitting and Trust Assessment to Improve the Security of Industrial Networks 43

Leveraging Data Fitting and Trust Assessment to Improve the ...

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43rd Chinese control Conference, CCC 2024

作者： Zhang, Heng Wang, Yinchu Xue, Yawen Deng, Ruilong Li, Hongran Zhang, Jian Jiangsu Ocean University School of Computer Engineering China Zhejiang University College of Control Science and Engineering China

ISBN: (纸本)9789887581581

Industrial network control systems (INCSs) are easy to be targeted by attackers due to their high economic value. Most of the existing defense methods are deployed at the network boundary, which causes high-security risks to INCS once an attacker enters the system. In addition, many existing intrusion detection systems (IDSs) build detection models based on historical data. When INCS lacks sufficient historical data, it is difficult to build detection models with high accuracy. In this paper, we propose a trust assessment model for INCS based on Fourier series fitting employing concept of zero trust to assess the real-time trust of INCS. The model alerts when INCS has low trust. We test two data injection attacks in the experiment to prove the effectiveness of our approach. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Network intrusion

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Dynamic Mode Decomposition for Individualized Model Predictive control with Application to Type 1 Diabetes 32

Dynamic Mode Decomposition for Individualized Model Predicti...

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32nd Mediterranean Conference on control and Automation, MED 2024

作者： Becchetti, Valentina Atanasious, Mohab M.H. Menegatti, Danilo Baldisseri, Federico Giuseppi, Alessandro Sapienza University of Rome Department of Computer Control and Management Engineering 'Antonio Ruberti' Italy

ISBN: (纸本)9798350395440

Type 1 diabetes is one of the main issues in current medical research. If not properly managed, it can lead to serious long-term complications such as blindness, stroke, coma and, ultimately, death. In recent years, research endeavors have focused on the development of a fully automated system for glucose control through automated insulin injections, known as Artificial Pancreas, which, however, is not yet robust enough for practical use. Currently, the UVA/Padova simulator provides the most accurate model for effectively representing the insulin-glucose dynamics of individual diabetic patients. As it is highly complex, consisting of 18 strongly nonlinear and time-varying equations, the objective of this work is to linearize it and reduce its dimensionality exploiting Dynamic Mode Decomposition with control. This allows for the synthesis of a controller based on Linear Model Predictive control to be applied to the original model. This is justified by the need to integrate the control algorithm into portable devices with limited computational capabilities. The proposed dimensionality reduction method is validated through in-vitro simulations showing that an accurate representation of the system dynamics is effectively preserved, while conveniently reducing required computational power. © 2024 IEEE.

关键词： Dynamic mode decomposition

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Two-Stage Distributionally Robust Optimization Model of Integrated Energy System Considering Flexible Resources 8

Two-Stage Distributionally Robust Optimization Model of Inte...

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8th IEEE International Conference on Energy Internet, ICEI 2024

作者： Li, Jie Deng, Dan Li, Yunzhi North China Electric Power University School of Control and Computer Engineering Beijing China

ISBN: (纸本)9798331523558

Due to the intermittent and fluctuating properties of renewable energy, Integrated Energy Systems (IES) face great challenges in terms of secure, economic, and low-carbon operation. To improve the penetration rate of renewable energy and the flexibility of IES, a two-stage distributionally robust optimization model is proposed considering flexible resources. Firstly, a fuzzy set of the total error function is established based on the Wasserstein distance. Then a two-stage distributionally robust optimization model is established within the day-ahead and real-time scales. Finally, the flexible resource model is finely established and the flexibility constraints are considered based on the flexibility demand of IES. The simulation results show that the upward and downward flexibility of IES can be maximally increased by 8 342.3 kW and 8 428.6 kW by means of flexible resources, meanwhile the system economy has not significantly decreased. © 2024 IEEE.

关键词： Clean energy

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Fault Warning of Wind Turbine Based on Optimal Hybrid Neural Network Prediction Model 5

Fault Warning of Wind Turbine Based on Optimal Hybrid Neural...

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5th International Conference on Advances in Electrical engineering and computer Applications, AEECA 2024

作者： Lv, Ruomeng North China Electric Power University School of Control and Computer Engineering Baoding China

ISBN: (纸本)9798350355253

Aiming at the problems of abnormal points and noise points existing in the original high-dimensional data of wind turbine system, and considering the strict requirements of fault warning on the accuracy of prediction model, a wind turbine fault warning method based on Bayes optimized hybrid neural network prediction model is proposed. In order to ensure the quality of modeling data, Ordering Points To Identify the Clustering Structure (OPTICS) and Isolation forest (iForest) were used to clean the pre-processed data. A "Convolutional Neural Network (CNN)"-"Long Short-Term Memory (LSTM)"-Attention hybrid model based on Bayesian optimization was established, and combined with the spatial feature extraction capability of CNN, the time series processing capability of LSTM and the weight allocation strategy of Attention mechanism, the accurate prediction of wind turbine under normal working conditions was realized through Bayesian optimization. The sliding window method is used to construct an early warning index, and its threshold is calculated by the kernel density estimation method to achieve reliable early warning and reduce the false alarm rate. Experiments with real fault data show that this method has excellent performance in improving prediction accuracy and reducing false positive rate. © 2024 IEEE.

关键词： Wind forecasting

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Semantic Explanations of Classifiers through the Ontology-Based Data Management Paradigm (Extended Abstract) 37

Semantic Explanations of Classifiers through the Ontology-Ba...

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37th International Workshop on Description Logics, DL 2024

作者： Papi, Laura Cima, Gianluca Console, Marco Lenzerini, Maurizio Department of Computer Control and Management Engineering 25 Via Ariosto Rome00185 Italy

One of the main challenges in modern AI systems is to explain the decisions of complex machine learning models, and recent years have seen a burgeoning of novel approaches. These approaches often rely on some structural components of the models under consideration, e.g., the set of features used for the classification task. As a result, explanations provided by these approaches are expressed in terms of the sub-symbolic information and, therefore, they are hard to interpret for users. In this paper, we argue that, in order to foster interpretability, these explanations should be expressed in terms of the knowledge that the users posses on the underlying application domain rather than on the sub-symbolic components of the model. To this end, our first contribution is the illustration of a novel formal framework for explaining the decisions of machine learning classifiers grounded on the Ontology-Based Data Management paradigm. Within this framework, explanations are defined by logical formulae using the symbols that an ontology defines and, as such, they posses a well-defined semantics. As a second contribution, we provide an algorithm that computes the best explanations that can be expressed in the class of conjunctive queries. © 2024 Copyright for this paper by its authors.

关键词： Adversarial machine learning

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Multi-dimensional networks as a tool to model, analyze, and interpret multi-subject brain connectivity in hyperscanning settings

Multi-dimensional networks as a tool to model, analyze, and ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Puxeddu, Maria Grazia Rinaldini, Greta Astolfi, Laura University of Rome La Sapienza Department of Computer Control and Management Engineering Rome Italy

ISBN: (纸本)9798350386226

Social interactions are woven in the fabric of our lives, from birth to adulthood. Studies show that when social connections are lacking, there is a high probability of developing poor mental and physical health. Conversely, some mental disorders result in a compromised ability to form these interactions, with autism being a prime example. For these reasons, understanding the neural mechanisms underlying social interactions is crucial. Hyperscanning experiments, where brain activity is simultaneously recorded from multiple subjects during social tasks, have emerged as a powerful tool for tackling this issue, and the study of brain-to-brain connectivity has provided significant insights into social neuroscience. However, no agreed-upon methods exist for analyzing and interpreting such data, and most of the analyses still rely on techniques designed for single-subject experiments. Here, we propose a framework to model brain-to-brain connectivity through multi-dimensional networks. Under this conceptualization, both intra- and inter-brain connectivity are viewed as components of a higher-order network that can be studied by extending graph theory indices to a multidimensional case. In this article, we also discuss how these indices can be interpreted in the context of multi-subject networks. Future investigations will apply this framework to hyperscanning datasets to provide new insights into the neural circuits associated with social interactions. © 2024 IEEE.

关键词： Diseases

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IMC Based PID controller for Spherical Tank Liquid Level control 1

IMC Based PID Controller for Spherical Tank Liquid Level Con...

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1st International Conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Kadu, C.B. Bhosale, V.P. Vikhe, P.S. Mandhare, V.V. Pravara Rural Engineering College Department of Instrumentation & Control Engineering Loni India Pravara Rural Engineering College Department of Computer Engineering Loni India

ISBN: (纸本)9798350355611

In the process industries, it's hard to control a non-linear process. Nonlinear behavior is frequently seen in real processes. The challenging problem of controlling a spherical tank is result of its nonlinearity caused by the continuously varying cross section. An IMC-based PID controller is employed to improve efficiency of controlling the level of spherical tanks which exhibit nonlinear dynamics. For processes up to second-order with dead time and first-order numerator dynamics, a single tuning parameter will be offered. The process model is determined employing mathematical modeling and standard step response-based system identification. The laboratory model of an interacting and non-interacting nonlinear system is introduced, which is capable of performing more complex tasks. To verify the effectiveness of the suggested IMC based PID controller, results of real-time experiments and a simulation study on spherical tank level control are incorporated. © 2025 IEEE.

关键词： Three term control systems

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Electricity theft detection model based on the CEEMDAN-CNN-ViT 2

Electricity theft detection model based on the CEEMDAN-CNN-V...

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2024 2nd International Conference on Power, Grid and Energy Storage, PGES 2024

作者： Yang, Hang Yang, Runkai Li, Xiang Zhou, Wenyuan School of Control and Computer Engineering North China Electric Power University Beijing China

Most of the traditional power theft detection methods construct the model directly on the basis of the original power sequences, and do not simultaneously consider the long-period dependencies in the long-time sequences and the local connectivity features between periods, which limits the deep excavation of the behavioral laws of power users. In order to further improve the accuracy of power theft detection, this paper proposes a high-precision power theft detection model that integrates local anomaly filtering, energy consumption decomposition, and multi-feature fusion strategies. First, local anomaly filtering is used to eliminate local anomalies in the normalized energy consumption data to avoid the misleading effect of short-term abnormal behavior on the model. Then, the energy consumption decomposition based on CEEMDAN selects specific frequency band data that can accurately characterize the pattern of power theft users to improve the accuracy of power theft detection. Next, the long-time periodic features in the two-dimensional data and the short-time local features in the one-dimensional sequences are integrated by multi-feature fusion to enhance the adaptability of the model. The results show that the proposed model can effectively improve the detection accuracy, detection completeness, F1 score, and accuracy compared with the existing methods. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Energy utilization

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Real-time and Energy-aware Scheduling for Edge-to-Cloud Continuum based on Reinforcement Learning 28

Real-time and Energy-aware Scheduling for Edge-to-Cloud Cont...

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28th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications, DS-RT 2024

作者： Panceri, Andrea Mattia, Gabriele Proietti Beraldi, Roberto Sapienza University of Rome Control and Management Engineering "A. Ruberti" Department of Computer Italy

ISBN: (纸本)9798331527211

By spreading out computing workloads over multiple levels, the Edge-to-Cloud continuum paradigm improves the performance of applications that are sensitive to latency. However, real-time scheduling is difficult on the Edge computing layer since it consists of a variety of nodes with varying uptime. In this paper, we address this issue by proposing an online and adaptive scheduling algorithm based on a continuous learning Reinforcement Learning. Our algorithm determines each work request individually, optimizing scheduling policies to meet real-time application requirements while taking environmental energy and battery limits into account. We validate the efficacy of our approach in dynamically assigning tasks, particularly in scenarios where Edge nodes exhibit variable speeds and unpredictable failures, while efficiently managing energy resources and battery constraints through extensive simulations and comparisons with static scheduling strategies. © 2024 IEEE.

关键词： Scheduling algorithms

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