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13th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2021

作者： Gupta, Rajesh Rathod, Tejal Reebadiya, Dakshita Tanwar, Sudeep Bhattacharya, Pronaya Pricop, Emil Nirma University Dept. of Computer Science and Engg. Ahmedabad India Petroleum-Gas University of Ploiesti Automatic Control Comp. and Elect. Dept Ploiesti Romania

ISBN: (纸本)9781665425346

The requirement of Device-to-device (D2D) communication is increasing day-by-day for rapid data transmission by bypassing the base station. Communication can be established either through cooperative devices or non-cooperative devices. Cooperative devices relay the information to the weak user, but the non-cooperative behavior of some intermediate nodes leads to malicious activities like fraud, misguide, and data-threat. In non-cooperative communication, the source node directly transmits data to the destination node (with a direct link) to eliminate the intermediate nodes for higher trust. But, such communications are vulnerable to various security and privacy threats such as spoofing, data modification, etc. due to the long-distance coverage of signal from source to destination. To overcome these security issues, this paper proposes a blockchain-based trusted network for non-cooperative D2D communication in a heterogeneous network, FaitH. Here, the devices are communicating via a secured blockchain network, where all the involved peer devices validate each transmission of data. Blockchain records each activity performed by any D2D node in communication. It helps to detect the malicious behavior of any D2D user. Each device is connected to a smart contract having some predefined rules. FaitH ensures secure, trusted, and reliable communication over traditional systems. © 2021 IEEE.

关键词： Smart contract

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Sample Complexity for Evaluating the Robust Linear Observers Performance under Coprime Factors Uncertainty

arXiv

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arXiv 2022年

作者： Zhang, Yifei Ukil, Sourav Kumar Sperilă, Andrei Sabău, Şerban Department of Electrical and Computer Engineering Stevens Institute of Technology NJ07030 United States Department of Automatic Control and Computer Science "Politehnica" University of Bucharest Bucharest Romania

This paper addresses the end-to-end sample complexity bound for learning in closed loop the state estimator-based robust H2 controller for an unknown (possibly unstable) Linear Time Invariant (LTI) system, when given a fixed state-feedback gain. We build on the results from Ding et al. (1994) to bridge the gap between the parameterization of all state-estimators and the celebrated Youla parameterization. Refitting the expression of the relevant closed loop allows for the optimal linear observer problem given a fixed state feedback gain to be recast as a convex problem in the Youla parameter. The robust synthesis procedure is performed by considering bounded additive model uncertainty on the coprime factors of the plant, such that a min-max optimization problem is formulated for the robust H2 controller via an observer approach. The closed-loop identification scheme follows Zhang et al. (2021), where the nominal model of the true plant is identified by constructing a Hankel-like matrix from a single time-series of noisy, finite length input-output data by using the ordinary least squares algorithm from Sarkar et al. (2020). Finally, a H∞ bound on the estimated model error is provided, as the robust synthesis procedure requires bounded additive uncertainty on the coprime factors of the model. Copyright © 2022, The Authors. All rights reserved.

关键词： Additives

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Custom-made Near Infrared Spectroscope as a Tool for Obtaining Information Regarding the Brain Condition

Custom-made Near Infrared Spectroscope as a Tool for Obtaini...

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International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Anna Wieczorek Edward Jacek Gorzelańczyk Mariusz Pelc Saravanakumar Duraisamy Luis A. Leiva Aleksandra Kawala-Sterniuk Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland Institute of Philosophy Kazimierz Wielki University Bydgoszcz Poland Department of Computer Science University of Luxembourg Luxembourg Luxembourg

We can obtain valuable information about the human brain using functional Near Infrared Spectroscopy (fNIRS). This paper describes the theoretical basis associated with this neuroimaging method through a custom-made prototype of a single-channel fNIRS device. The optodes were soldered to a milled Printed Circuit Board (PCB) and enclosed in a 3D printed housing. Using this fNIRS device, we performed a preliminary study to measure emotional responses from participants. Our results suggest that fNIRS allows for accurate measurement of emotions evoked by positive and negative images.

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Efficient downlink power allocation algorithms for cell-free massive mimo systems

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2021年 2卷 168-186页

作者： Chakraborty, Sucharita Demir, Ozlem Tugfe Bjornson, Emil Giselsson, Pontus Linköping University Linköping Sweden Space Application Centre Indian Space Research Organization Ahmedabad380015 India Department of Computer Science Kth Royal Institute of Technology Stockholm100 44 Sweden Department of Automatic Control Lund University Lund Sweden

Cell-free Massive MIMO systems consist of a large number of geographically distributed access points (APs) that serve the users by coherent joint transmission. The spectral efficiency (SE) achieved by each user depends on the power allocation: which APs that transmit to which users and with what power. In this article, we revisit the max-min and sum-SE power allocation policies, which have previously been approached using high-complexity general-purpose solvers. We develop and compare several different high-performance low-complexity power allocation algorithms that are appropriate for use in large systems. We propose two new algorithms for sum-SE power optimization inspired by weighted minimum mean square error (WMMSE) minimization and fractional programming (FP). Further, one new FP-based algorithm is proposed for max-min fair power allocation. The alternating direction method of multipliers (ADMM) is used to solve specific convex subproblems in the proposed algorithms. Our ADMM reformulations lead to multiple small-sized subproblems with closed-form solutions. The proposed algorithms find global or local optimal power allocation solutions for large-scale systems but with reduced computational time compared to previous work. © 2020 IEEE.

关键词： Mean square error

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H control for General Conformable Polynomial Fuzzy Models

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International Journal of Fuzzy Systems 2025年 1-10页

作者： Laajimi, Malek Gassara, Hamdi Rhaima, Mohamed Mchiri, Lassaad Makhlouf, Abdellatif Ben Laboratory of Sciences and Techniques of Automatic control & Computer Engineering National School of Engineering of Sfax Sfax Tunisia Department of Statistics and Operations Research College of Science King Saud University Riyadh Saudi Arabia Panthéon-Assas University Paris II Paris France Department of Mathematics Faculty of Sciences Sfax University Sfax Tunisia

This research focuses on exploring the exponential $$H_\infty $$ stability of general conformable nonlinear system. In order to address the nonlinearity inherent in the system, a polynomial fuzzy (PF) method is employed. Modeling a general conformable nonlinear system within the polynomial framework reduces the number of fuzzy rules compared to the classical Takagi–Sugeno fuzzy (TSF). Furthermore, controlling such a complex system, which accounts for perturbations, employs a PF model instead TSF model to describe its nonlinear dynamics, and incorporates a general conformable derivative instead of an integer-order one, is significantly more challenging, and remains unaddressed in previous studies. In this paper, a PF controller is designed in the form of sum of squares (SOS) to enhance the resilience against perturbations and ensure the exponential stability of the proposed model. The proposed SOS can be solved numerically, and partially symbolically, using the recently developed SOSTOOLS. In order to ensure the $$H_\infty $$ performance, a generalized criterion is defined for the general conformable nonlinear system. To demonstrate the effectiveness of the proposed method, a numerical example is provided.

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Ranking of attributes—comparative study based on data from stylometric domain

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Procedia computer science 2022年 207卷 2737-2746页

作者： Beata Zielosko Urszula Stańczyk Krzysztof Żabiński Institute of Computer Science University of Silesia in Katowice Będzińska 39 41-200 Sosnowiec Poland Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland

The area of feature selection methods constantly expands along with the development of artificial intelligence domain, and has great impact on almost every field, whenever data is processed and explored. The paper presents research where a ranking method was proposed, inspired by an approach which comes from an algorithm for induction of decision rules. The ranking procedure was based on calculation of standard deviation for attributes, taking into account assigned class labels. This method was compared with another ranking mechanism, a modified version of popular Relief algorithm, with incorporating characteristics of variables by supervised discretisation. Comparison of obtained results included the aspect of knowledge representation as well as the perspective of the accuracy for constructed rule-based classifiers. The experiments were performed on datasets from stylometry domain, where authorship attribution was considered as a classification task, and stylometric descriptors as characteristic features defining writing styles of authors.

关键词： Feature selection Ranking of attributes Relief Standard deviation Decision rules Stylometry Supervised discretisation Authorship attribution

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Active Exploration in Iterative Gaussian Process Regression for Uncertainty Modeling in Autonomous Racing

arXiv

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arXiv 2023年

作者： Benciolini, Tommaso Tang, Chen Leibold, Marion Weaver, Catherine Tomizuka, Masayoshi Zhan, Wei The Automatic Control Engineering The Technical University of Munich Germany The Department of Computer Science The University of Texas AustinTX United States The Department of Mechanical Engineering The University of California BerkeleyCA United States

Autonomous racing creates challenging control problems, but Model Predictive control (MPC) has made promising steps toward solving both the minimum lap-time problem and head-to-head racing. Yet, accurate models of the system are necessary for model-based control, including models of vehicle dynamics and opponent behavior. Both dynamics model error and opponent behavior can be modeled with Gaussian Process (GP) regression. GP models can be updated iteratively from data collected using the controller, but the strength of the GP model depends on the diversity of the training data. We propose a novel active exploration mechanism for iterative GP regression that purposefully collects additional data at regions of higher uncertainty in the GP model. In the exploration, a MPC collects diverse data by balancing the racing objectives and the exploration criterion;then the GP is re-trained. The process is repeated iteratively;in later iterations, the exploration is deactivated, and only the racing objectives are optimized. Thus, the MPC can achieve better performance by leveraging the improved GP model. We validate our approach in the highly realistic racing simulation platform Gran Turismo Sport of Sony Interactive Entertainment Inc for a minimum lap time challenge, and in numerical simulation of head-to-head. Our active exploration mechanism yields a significant improvement in the GP prediction accuracy compared to previous approaches and, thus, an improved racing performance. © 2023, CC BY-NC-ND.

关键词： Model predictive control

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Partial-Node-Based State Estimation for Complex Networks With Energy Harvesting Sensors

Partial-Node-Based State Estimation for Complex Networks Wit...

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International Conference on Automation and Computing (ICAC)

作者： Yu-Ang Wang Zidong Wang Fan Wang Lei Zou College of Information Science and Technology Donghua University Shanghai China Department of Computer Science Brunel University London Uxbridge Middlesex United Kingdom Institute for Automatic Control and Complex Systems (AKS) University of Duisburg-Essen Duisburg Germany

In this paper, the recursive partial-node-based (PN-B) state estimation problem is investigated for a class of time-varying complex networks (CNs) with energy harvesting sensors (EHSs). Measurements are transmitted to the remote state estimator by the EHSs only when the current energy level can compensate for the energy consumption. Only a part of the network nodes' measurements, which are available to the users, are utilized to estimate the network states. The objective of this problem is to design a recursive state estimator for CNs with EHSs based on available measurements observed from partial nodes. The upper bound of the estimation error covariance is first established with the help of intensive scholastic techniques and induction approach. Then, the required estimator gain is determined by minimizing such an upper bound. Finally, an illustrative example is presented to verify the effectiveness of the proposed PNB state estimation method.

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Enhancing Effluent Quality Predictions in Wastewater Treatment with LSTM Neural Network

Enhancing Effluent Quality Predictions in Wastewater Treatme...

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International Conference on System Theory, control, and Computing (ICSTCC)

作者： Daniel Voipan Iulian Vasiliev Larisa Diaconu Andreea-Elena Voipan Marian Barbu Department of Computer Science and Information Technology "Dunărea de Jos" University of Galați Galați Romania Department of Automatic Control and Electrical Engineering "Dunărea de Jos" University of Galați Galați Romania

ISBN: (数字)9798350364293

ISBN: (纸本)9798350364309

This research paper delves into the application of Long Short-Term Memory (LSTM) neural networks within the Benchmark Simulation Model No. 2 (BSM2) to enhance the predictability and efficiency of wastewater treatment processes. The study aims to develop advanced predictive models that can simulate the dynamics of wastewater treatment more accurately and adjust operational strategies dynamically. By integrating LSTM networks, the research enables continuous prediction of Effluent Quality Index (EQI) variables under stochastic and deterministic scenarios, thereby improving the accuracy and efficiency of predicting pollutant levels. The research uses an LSTM model to learn from a comprehensive dataset derived from historical simulations of BSM2, where key parameters such as the oxygen transfer coefficient (K L a) are systematically varied to measure their impact on effluent quality. The LSTM's capability to handle complex, nonlinear data and its adaptability to time series forecasting significantly enhances model performance, offering a robust tool for real-time decision-making and process optimization in wastewater treatment facilities. This approach not only improves the accuracy and efficiency of predicting pollutant levels but also supports environmental compliance and operational sustainability, making it a valuable tool for environmental engineers and professionals in the field of wastewater treatment.

关键词： Adaptation models Accuracy Time series analysis Stochastic processes Effluents Predictive models Real-time systems Wastewater Wastewater treatment Long short term memory

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Insights into automation of construction process using parallel-kinematic manipulators

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Construction Robotics 2023年第1期7卷 3-18页

作者： Klöckner, M. Haage, M. Eriksson, H. Malm, H. Nilsson, K. Robertsson, A. Andersson, R. Department of Computer Science Lund University Lund Sweden Winsome Consulting AB Lomma Sweden Fojab arkitekter AB Malmö Sweden Cognibotics AB Lund Sweden Department of Automatic Control Lund University Lund Sweden AChoice AB Malmö Sweden

This article discusses challenges, experiences and lessons learned so far while transforming a masonry build system based mostly on manual labour into a robot automated build system. Our motivation for selection of this masonry process is to try out how robot automation could impact the architects in their design work by providing a tool to directly manipulate wall expression down to individual brick level. Such manipulation is often much too costly for manual labour today. Moreover, masonry is a challenging application to automate. Understanding the manual processes involved and transforming them into automation equivalents faces several challenges; among them handling and distribution of the different materials involved, selection of tooling, sensing for handling of variation and digital tooling for the programming of the process. A novel parallel-kinematic manipulator (PKM) with computerized numerical control (CNC) is used as target for experiments, because the performance properties in stiffness, workspace and accuracy will allow us to extend work into further construction processes involving heavy and dirty manual labour.

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