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A comparison of Krylov methods for Shifted Skew-Symmetric Systems

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arXiv 2023年

作者： Idema, R. Vuik, C. Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft Institute of Applied Mathematics Mekelweg 4 Delft2628 CD Netherlands

It is well known that for general linear systems, only optimal Krylov methods with long recurrences exist. For special classes of linear systems it is possible to find optimal Krylov methods with short recurrences. In this paper we consider the important class of linear systems with a shifted skew-symmetric coefficient matrix. We present the MRS3 solver, a minimal residual method that solves these problems using short vector recurrences. We give an overview of existing Krylov solvers that can be used to solve these problems, and compare them with the MRS3 method, both theoretically and by numerical experiments. From this comparison we argue that the MRS3 solver is the fastest and most robust of these Krylov method for systems with a shifted skew-symmetric coefficient matrix. © 2023, CC BY.

关键词： Linear systems

Discharging Stage Prediction of Lithium Ion Battery Using CNN - DenseNet Architecture

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Discharging Stage Prediction of Lithium Ion Battery Using CN...

International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Mukhidin Akhmad Fauzan Ali Muhammad Abdillah Hayati Yassin Erly Krisnanik Wahyu Caesarendra Faculty of Integrated Technologies Universiti Brunei Darussalam Bandar Seri Begawan Brunei Darussalam Department of Electrical Engineering Pertamina University Jakarta Indonesia Faculty of Computer Science Veterans National Development University Jakarta Jakarta Indonesia Faculty of Mechanical Engineering Opole University of Technology Opole Poland

ISBN: (数字)9798331533243

ISBN: (纸本)9798331533250

Lithium-ion batteries are one of the most widely used types of batteries in various applications, including electronic devices, electric vehicles, and energy storage systems. However, battery discharging caused by parasitic reactions between electrodes and electrolytes is one of the most common problems. New methods such as Convolutional Neural Networks (CNN) can help predict discharging modes and improve battery reliability and safety. CNN is used to predict discharging in lithium-ion batteries based on data collected during battery monitoring experiments at different load levels. The data is processed and modeled to support the predictive model. The methodological process involves data collection, data preprocessing, feature selection, and artificial intelligence model training. Model performance is tested using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) as metrics. The developed CNN model is observed to follow data trends. For the CNN model, an RMSE of 0.0255, MAE of 0.0182, and MAPE of 0.216% were obtained during the training process on 80% of the data for voltage prediction. The model has been tested to be applicable in real-world scenarios for detecting battery discharging.

关键词： Lithium-ion batteries Measurement Computational modeling computer architecture Voltage Predictive models Data models Convolutional neural networks Long short term memory Load modeling

Combating Inter-Operator Pilot Contamination in Reconfigurable Intelligent Surfaces Assisted Multi-Operator Networks

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arXiv 2023年

作者： Gürgünoglu, Doga Björnson, Emil Fodor, Gábor Faculty of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden Ericsson Research Stockholm164 80 Sweden

In this paper, we study a new kind of pilot contamination appearing in multi-operator reconfigurable intelligent surfaces (RIS) assisted networks, where multiple operators provide services to their respective served users. The operators use dedicated frequency bands, but each RIS inadvertently reflects the transmitted uplink signals of the user equipment devices in multiple bands. Consequently, the concurrent reflection of pilot signals during the channel estimation phase introduces a new inter-operator pilot contamination effect. We investigate the implications of this effect in systems with either deterministic or correlated Rayleigh fading channels, specifically focusing on its impact on channel estimation quality, signal equalization, and channel capacity. The numerical results demonstrate the substantial degradation in system performance caused by this phenomenon and highlight the pressing need to address inter-operator pilot contamination in multi-operator RIS deployments. To combat the negative effect of this new type of pilot contamination, we propose to use orthogonal RIS configurations during uplink pilot transmission, which can mitigate or eliminate the negative effect of inter-operator pilot contamination at the expense of some inter-operator information exchange and orchestration. © 2023, CC BY.

关键词： Channel estimation

Large-scale Online Ridesharing: The Effect of Assignment Optimality on System Performance

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arXiv 2023年

作者： Fiedler, David Čertický, Michal Alonso-Mora, Javier Pěchouček, Michal Čáp, Michal Department of Computer Science Faculty of Electrical Engineering CTU in Prague Czech Republic Department of Cognitive Robotics 3ME TU Delft Netherlands

Mobility-on-demand (MoD) systems consist of a fleet of shared vehicles that can be hailed for one-way point-to-point trips. The total distance driven by the vehicles and the fleet size can be reduced by employing ridesharing, i.e., by assigning multiple passengers to one vehicle. However, finding the optimal passenger-vehicle assignment in an MoD system is a hard combinatorial problem. In this work, we demonstrate how the VGA method, a recently proposed systematic method for ridesharing, can be used to compute the optimal passenger-vehicle assignments and corresponding vehicle routes in a massive-scale MoD system. In contrast to existing works, we solve all passenger-vehicle assignment problems to optimality, regularly dealing with instances containing thousands of vehicles and passengers. Moreover, to examine the impact of using optimal ridesharing assignments, we compare the performance of an MoD system that uses optimal assignments against an MoD system that uses assignments computed using insertion heuristic and against an MoD system that uses no ridesharing. We found that the system that uses optimal ridesharing assignments subject to the maximum travel delay of 4 minutes reduces the vehicle distance driven by 57 % compared to an MoD system without ridesharing. Furthermore, we found that the optimal assignments result in a 20 % reduction in vehicle distance driven and 5 % lower average passenger travel delay compared to a system that uses insertion heuristic. © 2023, CC BY-NC-SA.

关键词： Vehicle routing

Federated Learning from Heterogeneous Data via Controlled Bayesian Air Aggregation

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arXiv 2023年

作者： Gafni, Tomer Cohen, Kobi Eldar, Yonina C. School of Electrical and Computer Engineering Ben-Gurion University of the Negev Beer-Sheva Israel Math and CS Faculty Weizmann Institute of Science Rehovot Israel

Federated learning (FL) is an emerging machine learning paradigm for training models across multiple edge devices holding local data sets, without explicitly exchanging the data. Recently, over-the-air (OTA) FL has been suggested to reduce the bandwidth and energy consumption, by allowing the users to transmit their data simultaneously over a Multiple Access Channel (MAC). However, this approach results in channel noise directly affecting the optimization procedure, which may degrade the accuracy of the trained model. In this paper we jointly exploit the prior distribution of local weights and the channel distribution, and develop an OTA FL algorithm based on a Bayesian approach for signal aggregation. Our proposed algorithm, dubbed Bayesian Air Aggregation Federated learning (BAAF), is shown to effectively mitigate noise and fading effects induced by the channel. To handle statistical heterogeneity of users data, which is a second major challenge in FL, we extend BAAF to allow for appropriate local updates by the users and develop the Controlled Bayesian Air Aggregation Federated-learning (COBAAF) algorithm. In addition to using a Bayesian approach to average the channel output, COBAAF controls the drift in local updates using a judicious design of correction terms. We analyze the convergence of the learned global model using BAAF and COBAAF in noisy and heterogeneous environment, showing their ability to achieve a convergence rate similar to that achieved over error-free channels. Simulation results demonstrate the improved convergence of BAAF and COBAAF over existing algorithms in machine learning tasks. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy utilization

Proof of Equation: A Novel Consensus Algorithm for Dynamic Spectrum Access

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Proof of Equation: A Novel Consensus Algorithm for Dynamic S...

AFRICON Conference

作者： Thiwanka Silva Madhushika Bamunuge Dilusha Dissanayake Chatura Seneviratne Tharindu Gamage Madhusanka Liyanage Dept. of Electrical and Information Eng. Faculty of Engineering University of Ruhuna Sri Lanka School of Computer Science University College of Dublin Ireland

The future of communication technology is moving from 5G to 6G with new innovations. Blockchain (BC) is a such immersive technology that significantly impacts the betterment of communication technology. BC-based spectrum-sharing solutions can be used in Dynamic Spectrum Access (DSA) systems to fulfill the need for secure and efficient communication. With the invention of cognitive radio networks, DSA became a popular topic for the scientific community. Spectrum misuse/violations can occur due to the rapid growth of spectrum sharing. As the system is open to malicious attacks, licensed spectrum owners must be identified and verified. However, the existing BC-based DSA solutions are more expensive, non-optimized, and lack spectrum misuse detection. This paper proposes a novel consensus algorithm called “Proof of Equation” for spectrum misuse detection. The core of the proposed algorithm is a consensus score calculation based on a numerical equation with three parameters rather than using cryptographic calculations. The performance of the proposed algorithm is studied using Python simulations, and simulation results show that the proposed algorithm outperforms the Proof of Work (PoW) and Proof of Stake (PoS) consensus algorithms in terms of block production time.

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VARIETIES OF QUANTITATIVE OR CONTINUOUS ALGEBRAS (EXTENDED ABSTRACT)

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arXiv 2023年

作者： Adámek, Jiří Dostál, Matěj Velebil, Jiří Department of Mathematics Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Institute for Theoretical Computer Science Technical University Braunschweig Germany

Quantitative algebras are algebras enriched in the category Met of metric spaces so that all operations are nonexpanding. Mardare, Plotkin and Panangaden introduced varieties (aka 1-basic varieties) as classes of quantitative algebras presented by quantitative equations. We prove that they bijectively correspond to strongly finitary monads T on Met. This means that T is the Kan extension of its restriction to finite discrete spaces. An analogous result holds in the category CMet of complete metric spaces. Analogously, continuous algebras are algebras enriched in CPO, the category of ω-cpos, so that all operations are continuous. We introduce equations between extended terms, and prove that varieties (classes presented by such equations) correspond bijectively to strongly finitary monads T on CPO. This means that T is the Kan extension of its restriction to finite discrete cpos. (The two results have substantially different proofs.) An analogous result is also presented for monads on DCPO. We also characterize strong finitarity in all the categories above by preservations of certain weighted colimits. As a byproduct we prove that directed colimits commute with finite products in all cartesian closed categories. Copyright © 2023, The Authors. All rights reserved.

关键词： Algebra

Deep Generative Model with Isolation Forest (DGM-IF) for Unsupervised Anomaly Detection in Wireless Sensor Network and Internet of Things

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Deep Generative Model with Isolation Forest (DGM-IF) for Uns...

International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Meriem Zerkouk Miloud Mihoubi Belkacem Chikhaoui Faculty of Electrical Engineering Mathematics and Computer Science University of Sherbrooke Enschede The Netherlands Department of Information technology University of TÉLUQ Montréal Canada

Anomaly detection is crucial for maintaining the reliability and security of wireless sensor networks and loT systems. Conventional methods require labeled data, often unavailable in these systems, making unsupervised techniques essential. Deep learning has shown promise in unsupervised anomaly detection tasks, including wireless sensor networks and loT applications. The Isolation Forest, an unsupervised anomaly detection algorithm, isolates anomalies based on in-herent properties. Combining deep learning with the Isolation Forest can improve accuracy and effectiveness in detecting anomalies. This paper presents the Deep Generative Model with Isolation Forest (DGM-IF), a novel unsupervised anomaly detection method for wireless sensor networks and loT. DGM-IF integrates deep generative models with the Isolation Forest algorithm to learn a robust representation of normal data and identify anomalies. The model generates synthetic data based on the learned distribution and employs the Isolation Forest to separate deviating data points. The proposed technique is assessed using real-world datasets and benchmarked against cutting-edge methods, proving its efficacy in detecting anomalies. The DGM-IF approach has the potential to significantly enhance the reliability and security of wireless sensor networks and IoT systems by identifying potential threats and attacks.

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FOGO - an Optimized Fog and Edge Computing Method for VANETs

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FOGO - an Optimized Fog and Edge Computing Method for VANETs

Proceedings of the International Convention MIPRO

作者： Renato Ćorluka Ivona Matoš Davor Damjanović Josip Balen Faculty of Electrical Engineering Computer Science and Information Technology Osijek Josip Juraj Strossmayer University of Osijek Kneza Trpimira 2b Osijek

Vehicular Ad-hoc Networks (VANETs) have many specific issues and challenges due to a heterogeneous and highly dynamic environment, and therefore traditional solutions need to be improved and adopted in order to satisfy the networking and processing requirements. Fog and edge computing principles enable VANETs to achieve a more realistic and dependable architecture by using several layers for information processing. In this paper, the developed FOGO method is improved and customized for VANETs. The proposed solution is using an enhanced infrastructure in which additional mobile fog nodes are added to the network together with the existing stationary nodes. Small and medium sized messages are processed by mobile fog nodes, which then disseminate the results across the network. Roadside units (RSUs) are fixed, highly-capable devices positioned throughout the network to assist the processing of increasing volumes of data. Additionally, the cloud is taken into account, but only when processing large amounts of data. Along with the presented method flowcharts, operating algorithms, and message structure, the proposed system architecture is described. Furthermore, potential use cases are suggested together with the metrics for the system performance evaluation. The LuST scenario was used to evaluate the proposed architecture and the results showed improved message processing efficiency.

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