The nonlinearities of soft robots bring control challenges like hysteresis but also provide them with computational capacities. This paper introduces a fuzzy pneumatic physical reservoir computing (FPRC) model for feedforward hysteresis compensation in motion tracking control of soft actuators. Our method utilizes a pneumatic bending actuator as a physical reservoir with nonlinear computing capacities to control another pneumatic bending actuator. The FPRC model employs a Takagi-Sugeno (T-S) fuzzy model to process outputs from the physical reservoir. In comparative evaluations, the FPRC model shows equivalent training performance to an Echo State Network (ESN) model, whereas it exhibits better test accuracies with significantly reduced execution time. Experiments validate the proposed FPRC model's effectiveness in controlling the bending motion of the pneumatic soft actuator with open and closed-loop control systems. The proposed FPRC model's robustness against environmental disturbances has also been experimentally verified. To the authors' knowledge, this is the first implementation of a physical system in the feedforward hysteresis compensation model for controlling soft actuators. This study is expected to advance physical reservoir computing in nonlinear control applications and extend the feedforward hysteresis compensation methods for controlling soft actuators.
{"title":"Control Pneumatic Soft Bending Actuator with Feedforward Hysteresis Compensation by Pneumatic Physical Reservoir Computing","authors":"Junyi Shen, Tetsuro Miyazaki, Kenji Kawashima","doi":"arxiv-2409.06961","DOIUrl":"https://doi.org/arxiv-2409.06961","url":null,"abstract":"The nonlinearities of soft robots bring control challenges like hysteresis\u0000but also provide them with computational capacities. This paper introduces a\u0000fuzzy pneumatic physical reservoir computing (FPRC) model for feedforward\u0000hysteresis compensation in motion tracking control of soft actuators. Our\u0000method utilizes a pneumatic bending actuator as a physical reservoir with\u0000nonlinear computing capacities to control another pneumatic bending actuator.\u0000The FPRC model employs a Takagi-Sugeno (T-S) fuzzy model to process outputs\u0000from the physical reservoir. In comparative evaluations, the FPRC model shows\u0000equivalent training performance to an Echo State Network (ESN) model, whereas\u0000it exhibits better test accuracies with significantly reduced execution time.\u0000Experiments validate the proposed FPRC model's effectiveness in controlling the\u0000bending motion of the pneumatic soft actuator with open and closed-loop control\u0000systems. The proposed FPRC model's robustness against environmental\u0000disturbances has also been experimentally verified. To the authors' knowledge,\u0000this is the first implementation of a physical system in the feedforward\u0000hysteresis compensation model for controlling soft actuators. This study is\u0000expected to advance physical reservoir computing in nonlinear control\u0000applications and extend the feedforward hysteresis compensation methods for\u0000controlling soft actuators.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142217818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Changseob Lee, Ikhyeon Kwon, Anirban Samanta, Siwei Li, S. J. Ben Yoo
3T TRAM with doping profile (P+PNPNN+) is experimentally demonstrated on a silicon photonic platform. By using additional implant layers, this device provides excellent memory performance compared to the conventional structure (PNPN). TCAD is used to reflect the physical behavior, and the high-speed memory operations are described through the model.
{"title":"High Performance Three-Terminal Thyristor RAM with a P+/P/N/P/N/N+ Doping Profile on a Silicon-Photonic CMOS Platform","authors":"Changseob Lee, Ikhyeon Kwon, Anirban Samanta, Siwei Li, S. J. Ben Yoo","doi":"arxiv-2409.07598","DOIUrl":"https://doi.org/arxiv-2409.07598","url":null,"abstract":"3T TRAM with doping profile (P+PNPNN+) is experimentally demonstrated on a\u0000silicon photonic platform. By using additional implant layers, this device\u0000provides excellent memory performance compared to the conventional structure\u0000(PNPN). TCAD is used to reflect the physical behavior, and the high-speed\u0000memory operations are described through the model.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142217800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ali Forootani, Danial Esmaeili Aliabadi, Daniela Thraen
This article introduces Bio-Eng-LMM AI chatbot, a versatile platform designed to enhance user interaction for educational and research purposes. Leveraging cutting-edge open-source Large Language Models (LLMs), Bio-Eng-LMM operates as a sophisticated AI assistant, exploiting the capabilities of traditional models like ChatGPT. Central to Bio-Eng-LMM is its implementation of Retrieval Augmented Generation (RAG) through three primary methods: integration of preprocessed documents, real-time processing of user-uploaded files, and information retrieval from any specified website. Additionally, the chatbot incorporates image generation via a Stable Diffusion Model (SDM), image understanding and response generation through LLAVA, and search functionality on the internet powered by secure search engine such as DuckDuckGo. To provide comprehensive support, Bio-Eng-LMM offers text summarization, website content summarization, and both text and voice interaction. The chatbot maintains session memory to ensure contextually relevant and coherent responses. This integrated platform builds upon the strengths of RAG-GPT and Web-Based RAG Query (WBRQ) where the system fetches relevant information directly from the web to enhance the LLMs response generation.
{"title":"Bio-Eng-LMM AI Assist chatbot: A Comprehensive Tool for Research and Education","authors":"Ali Forootani, Danial Esmaeili Aliabadi, Daniela Thraen","doi":"arxiv-2409.07110","DOIUrl":"https://doi.org/arxiv-2409.07110","url":null,"abstract":"This article introduces Bio-Eng-LMM AI chatbot, a versatile platform designed\u0000to enhance user interaction for educational and research purposes. Leveraging\u0000cutting-edge open-source Large Language Models (LLMs), Bio-Eng-LMM operates as\u0000a sophisticated AI assistant, exploiting the capabilities of traditional models\u0000like ChatGPT. Central to Bio-Eng-LMM is its implementation of Retrieval\u0000Augmented Generation (RAG) through three primary methods: integration of\u0000preprocessed documents, real-time processing of user-uploaded files, and\u0000information retrieval from any specified website. Additionally, the chatbot\u0000incorporates image generation via a Stable Diffusion Model (SDM), image\u0000understanding and response generation through LLAVA, and search functionality\u0000on the internet powered by secure search engine such as DuckDuckGo. To provide\u0000comprehensive support, Bio-Eng-LMM offers text summarization, website content\u0000summarization, and both text and voice interaction. The chatbot maintains\u0000session memory to ensure contextually relevant and coherent responses. This\u0000integrated platform builds upon the strengths of RAG-GPT and Web-Based RAG\u0000Query (WBRQ) where the system fetches relevant information directly from the\u0000web to enhance the LLMs response generation.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142217820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mixed Reality (MR) is becoming ubiquitous as it finds its applications in education, healthcare, and other sectors beyond leisure. While MR end devices, such as headsets, have low energy intensity, the total number of devices and resource requirements of the entire MR ecosystem, which includes cloud and edge endpoints, can be significant. The resulting operational and embodied carbon footprint of MR has led to concerns about its environmental implications. Recent research has explored reducing the carbon footprint of MR devices by exploring hardware design space or network optimizations. However, many additional avenues for enhancing MR's sustainability remain open, including energy savings in non-processor components and carbon-aware optimizations in collaborative MR ecosystems. In this paper, we aim to identify key challenges, existing solutions, and promising research directions for improving MR sustainability. We explore adjacent fields of embedded and mobile computing systems for insights and outline MR-specific problems requiring new solutions. We identify the challenges that must be tackled to enable researchers, developers, and users to avail themselves of these opportunities in collaborative MR systems.
{"title":"Scoping Sustainable Collaborative Mixed Reality","authors":"Yasra Chandio, Noman Bashir, Tian Guo, Elsa Olivetti, Fatima Anwar","doi":"arxiv-2409.07640","DOIUrl":"https://doi.org/arxiv-2409.07640","url":null,"abstract":"Mixed Reality (MR) is becoming ubiquitous as it finds its applications in\u0000education, healthcare, and other sectors beyond leisure. While MR end devices,\u0000such as headsets, have low energy intensity, the total number of devices and\u0000resource requirements of the entire MR ecosystem, which includes cloud and edge\u0000endpoints, can be significant. The resulting operational and embodied carbon\u0000footprint of MR has led to concerns about its environmental implications.\u0000Recent research has explored reducing the carbon footprint of MR devices by\u0000exploring hardware design space or network optimizations. However, many\u0000additional avenues for enhancing MR's sustainability remain open, including\u0000energy savings in non-processor components and carbon-aware optimizations in\u0000collaborative MR ecosystems. In this paper, we aim to identify key challenges,\u0000existing solutions, and promising research directions for improving MR\u0000sustainability. We explore adjacent fields of embedded and mobile computing\u0000systems for insights and outline MR-specific problems requiring new solutions.\u0000We identify the challenges that must be tackled to enable researchers,\u0000developers, and users to avail themselves of these opportunities in\u0000collaborative MR systems.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142217808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, an orthogonal mode decomposition method is proposed to decompose ffnite length real signals on both the real and imaginary axes of the complex plane. The interpolation function space of ffnite length discrete signal is constructed, and the relationship between the dimensionality of the interpolation function space and its subspaces and the band width of the interpolation function is analyzed. It is proved that the intrinsic mode is actually the narrow band signal whose intrinsic instantaneous frequency is always positive (or always negative). Thus, the eigenmode decomposition problem is transformed into the orthogonal projection problem of interpolation function space to its low frequency subspace or narrow band subspace. Different from the existing mode decomposition methods, the orthogonal modal decomposition is a local time-frequency domain algorithm. Each operation extracts a speciffc mode. The global decomposition results obtained under the precise deffnition of eigenmodes have uniqueness and orthogonality. The computational complexity of the orthogonal mode decomposition method is also much smaller than that of the existing mode decomposition methods.
{"title":"Orthogonal Mode Decomposition for Finite Discrete Signals","authors":"Ning Li, Lezhi Li","doi":"arxiv-2409.07242","DOIUrl":"https://doi.org/arxiv-2409.07242","url":null,"abstract":"In this paper, an orthogonal mode decomposition method is proposed to\u0000decompose ffnite length real signals on both the real and imaginary axes of the\u0000complex plane. The interpolation function space of ffnite length discrete\u0000signal is constructed, and the relationship between the dimensionality of the\u0000interpolation function space and its subspaces and the band width of the\u0000interpolation function is analyzed. It is proved that the intrinsic mode is\u0000actually the narrow band signal whose intrinsic instantaneous frequency is\u0000always positive (or always negative). Thus, the eigenmode decomposition problem\u0000is transformed into the orthogonal projection problem of interpolation function\u0000space to its low frequency subspace or narrow band subspace. Different from the\u0000existing mode decomposition methods, the orthogonal modal decomposition is a\u0000local time-frequency domain algorithm. Each operation extracts a speciffc mode.\u0000The global decomposition results obtained under the precise deffnition of\u0000eigenmodes have uniqueness and orthogonality. The computational complexity of\u0000the orthogonal mode decomposition method is also much smaller than that of the\u0000existing mode decomposition methods.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"82 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142217817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We propose a novel continuous-time algorithm for inequality-constrained convex optimization inspired by proportional-integral control. Unlike the popular primal-dual gradient dynamics, our method includes a proportional term to control the primal variable through the Lagrange multipliers. This approach has both theoretical and practical advantages. On the one hand, it simplifies the proof of the exponential convergence in the case of smooth, strongly convex problems, with a more straightforward assessment of the convergence rate concerning prior literature. On the other hand, through several examples, we show that the proposed algorithm converges faster than primal-dual gradient dynamics. This paper aims to illustrate these points by thoroughly analyzing the algorithm convergence and discussing some numerical simulations.
{"title":"A feedback control approach to convex optimization with inequality constraints","authors":"V. Cerone, S. M. Fosson, S. Pirrera, D. Regruto","doi":"arxiv-2409.07168","DOIUrl":"https://doi.org/arxiv-2409.07168","url":null,"abstract":"We propose a novel continuous-time algorithm for inequality-constrained\u0000convex optimization inspired by proportional-integral control. Unlike the\u0000popular primal-dual gradient dynamics, our method includes a proportional term\u0000to control the primal variable through the Lagrange multipliers. This approach\u0000has both theoretical and practical advantages. On the one hand, it simplifies\u0000the proof of the exponential convergence in the case of smooth, strongly convex\u0000problems, with a more straightforward assessment of the convergence rate\u0000concerning prior literature. On the other hand, through several examples, we\u0000show that the proposed algorithm converges faster than primal-dual gradient\u0000dynamics. This paper aims to illustrate these points by thoroughly analyzing\u0000the algorithm convergence and discussing some numerical simulations.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142217825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bogdan Vlahov, Jason Gibson, Manan Gandhi, Evangelos A. Theodorou
This paper introduces a new C++/CUDA library for GPU-accelerated stochastic optimization called MPPI-Generic. It provides implementations of Model Predictive Path Integral control, Tube-Model Predictive Path Integral Control, and Robust Model Predictive Path Integral Control, and allows for these algorithms to be used across many pre-existing dynamics models and cost functions. Furthermore, researchers can create their own dynamics models or cost functions following our API definitions without needing to change the actual Model Predictive Path Integral Control code. Finally, we compare computational performance to other popular implementations of Model Predictive Path Integral Control over a variety of GPUs to show the real-time capabilities our library can allow for. Library code can be found at: https://acdslab.github.io/mppi-generic-website/ .
{"title":"MPPI-Generic: A CUDA Library for Stochastic Optimization","authors":"Bogdan Vlahov, Jason Gibson, Manan Gandhi, Evangelos A. Theodorou","doi":"arxiv-2409.07563","DOIUrl":"https://doi.org/arxiv-2409.07563","url":null,"abstract":"This paper introduces a new C++/CUDA library for GPU-accelerated stochastic\u0000optimization called MPPI-Generic. It provides implementations of Model\u0000Predictive Path Integral control, Tube-Model Predictive Path Integral Control,\u0000and Robust Model Predictive Path Integral Control, and allows for these\u0000algorithms to be used across many pre-existing dynamics models and cost\u0000functions. Furthermore, researchers can create their own dynamics models or\u0000cost functions following our API definitions without needing to change the\u0000actual Model Predictive Path Integral Control code. Finally, we compare\u0000computational performance to other popular implementations of Model Predictive\u0000Path Integral Control over a variety of GPUs to show the real-time capabilities\u0000our library can allow for. Library code can be found at:\u0000https://acdslab.github.io/mppi-generic-website/ .","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"75 2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142217809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Anbo Tao, Yarong Luo, Chunxi Xia, Chi Guo, Xingxing Li
Pose estimation is a crucial problem in simultaneous localization and mapping (SLAM). However, developing a robust and consistent state estimator remains a significant challenge, as the traditional extended Kalman filter (EKF) struggles to handle the model nonlinearity, especially for inertial measurement unit (IMU) and light detection and ranging (LiDAR). To provide a consistent and efficient solution of pose estimation, we propose Eq-LIO, a robust state estimator for tightly coupled LIO systems based on an equivariant filter (EqF). Compared with the invariant Kalman filter based on the $SE_2(3)$ group structure, the EqF uses the symmetry of the semi-direct product group to couple the system state including IMU bias, navigation state and LiDAR extrinsic calibration state, thereby suppressing linearization error and improving the behavior of the estimator in the event of unexpected state changes. The proposed Eq-LIO owns natural consistency and higher robustness, which is theoretically proven with mathematical derivation and experimentally verified through a series of tests on both public and private datasets.
{"title":"Equivariant Filter for Tightly Coupled LiDAR-Inertial Odometry","authors":"Anbo Tao, Yarong Luo, Chunxi Xia, Chi Guo, Xingxing Li","doi":"arxiv-2409.06948","DOIUrl":"https://doi.org/arxiv-2409.06948","url":null,"abstract":"Pose estimation is a crucial problem in simultaneous localization and mapping\u0000(SLAM). However, developing a robust and consistent state estimator remains a\u0000significant challenge, as the traditional extended Kalman filter (EKF)\u0000struggles to handle the model nonlinearity, especially for inertial measurement\u0000unit (IMU) and light detection and ranging (LiDAR). To provide a consistent and\u0000efficient solution of pose estimation, we propose Eq-LIO, a robust state\u0000estimator for tightly coupled LIO systems based on an equivariant filter (EqF).\u0000Compared with the invariant Kalman filter based on the $SE_2(3)$ group\u0000structure, the EqF uses the symmetry of the semi-direct product group to couple\u0000the system state including IMU bias, navigation state and LiDAR extrinsic\u0000calibration state, thereby suppressing linearization error and improving the\u0000behavior of the estimator in the event of unexpected state changes. The\u0000proposed Eq-LIO owns natural consistency and higher robustness, which is\u0000theoretically proven with mathematical derivation and experimentally verified\u0000through a series of tests on both public and private datasets.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142217819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this note, we give a short information-theoretic proof of the consistency of the Gaussian maximum likelihood estimator in linear auto-regressive models. Our proof yields nearly optimal non-asymptotic rates for parameter recovery and works without any invocation of stability in the case of finite hypothesis classes.
{"title":"A Short Information-Theoretic Analysis of Linear Auto-Regressive Learning","authors":"Ingvar Ziemann","doi":"arxiv-2409.06437","DOIUrl":"https://doi.org/arxiv-2409.06437","url":null,"abstract":"In this note, we give a short information-theoretic proof of the consistency\u0000of the Gaussian maximum likelihood estimator in linear auto-regressive models.\u0000Our proof yields nearly optimal non-asymptotic rates for parameter recovery and\u0000works without any invocation of stability in the case of finite hypothesis\u0000classes.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142217839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bálint Hartmann, Tamás Soha, Michelle T. Cirunay, Tímea Erdei
Research on the vulnerability of electric networks with a complex network approach has produced significant results in the last decade, especially for transmission networks. These studies have shown that there are causal relations between certain structural properties of networks and their vulnerabilities, leading to an inherent weakness. The purpose of present work was twofold: to test the hypotheses already examined on evolving transmission networks and to gain a deeper understanding on the nature of these inherent weaknesses. For this, historical models of a medium-voltage distribution network supply area were reconstructed and analysed. Topological efficiency of the networks was calculated against node and edge removals of different proportions. We found that the tolerance of the evolving grid remained practically unchanged during the examined period, implying that the increase in size is dominantly caused by the connection of geographically and spatially constrained supply areas and not by an evolutionary process. We also show that probability density functions of centrality metrics, typically connected to vulnerability, show only minor variation during the early evolution of the examined distribution network, and in many cases resemble the properties of the modern days.
{"title":"Uncovering the inherited vulnerability of electric distribution networks","authors":"Bálint Hartmann, Tamás Soha, Michelle T. Cirunay, Tímea Erdei","doi":"arxiv-2409.06194","DOIUrl":"https://doi.org/arxiv-2409.06194","url":null,"abstract":"Research on the vulnerability of electric networks with a complex network\u0000approach has produced significant results in the last decade, especially for\u0000transmission networks. These studies have shown that there are causal relations\u0000between certain structural properties of networks and their vulnerabilities,\u0000leading to an inherent weakness. The purpose of present work was twofold: to\u0000test the hypotheses already examined on evolving transmission networks and to\u0000gain a deeper understanding on the nature of these inherent weaknesses. For\u0000this, historical models of a medium-voltage distribution network supply area\u0000were reconstructed and analysed. Topological efficiency of the networks was\u0000calculated against node and edge removals of different proportions. We found\u0000that the tolerance of the evolving grid remained practically unchanged during\u0000the examined period, implying that the increase in size is dominantly caused by\u0000the connection of geographically and spatially constrained supply areas and not\u0000by an evolutionary process. We also show that probability density functions of\u0000centrality metrics, typically connected to vulnerability, show only minor\u0000variation during the early evolution of the examined distribution network, and\u0000in many cases resemble the properties of the modern days.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"308 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142217670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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