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arXiv 2023年

作者： Aguiar, Miguel Das, Amritam Johansson, Karl H. Division of Decision and Control Systems and Digital Futures KTH Royal Institute of Technology Stockholm Sweden Control Systems Group Dept. of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands

We describe a recurrent neural network (RNN) based architecture to learn the flow function of a causal, time-invariant and continuous-time control system from trajectory data. By restricting the class of control inputs to piecewise constant functions, we show that learning the flow function is equivalent to learning the input-to-state map of a discrete-time dynamical system. This motivates the use of an RNN together with encoder and decoder networks which map the state of the system to the hidden state of the RNN and back. We show that the proposed architecture is able to approximate the flow function by exploiting the system’s causality and time-invariance. The output of the learned flow function model can be queried at any time instant. We experimentally validate the proposed method using models of the Van der Pol and FitzHugh-Nagumo oscillators. In both cases, the results demonstrate that the architecture is able to closely reproduce the trajectories of these two systems. For the Van der Pol oscillator, we further show that the trained model generalises to the system’s response with a prolonged prediction time horizon as well as control inputs outside the training distribution. For the FitzHugh-Nagumo oscillator, we show that the model accurately captures the input-dependent phenomena of excitability. © 2023, CC BY-NC-ND.

关键词： Recurrent neural networks

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Multi-Task Bias-Variance Trade-Off Through Functional Constraints

Multi-Task Bias-Variance Trade-Off Through Functional Constr...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Juan Cerviño Juan Andrés Bazerque Miguel Calvo-Fullana Alejandro Ribeiro Dept. of Electrical and Systems Engineering University of Pennsylvania Philadelphia USA Dept. of Electrical and Computer Engineering University of Pittsburgh Pittsburgh USA Dept. of Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain

Multi-task learning aims to acquire a set of functions, either regressors or classifiers, that perform well for diverse tasks. At its core, the idea behind multi-task learning is to exploit the intrinsic similarity across data sources to aid in the learning process for each individual domain. In this paper we draw intuition from the two extreme learning scenarios – a single function for all tasks, and a task-specific function that ignores the other tasks dependencies – to propose a bias-variance trade-off. To control the relationship between the variance (given by the number of i.i.d. samples), and the bias (coming from data from other task), we introduce a constrained learning formulation that enforces domain specific solutions to be close to a central function. This problem is solved in the dual domain, for which we propose a stochastic primal-dual algorithm. Experimental results for a multi-domain classification problem with real data show that the proposed procedure outperforms both the task specific, as well as the single classifiers.

关键词： Soft sensors Signal processing algorithms Signal processing Multitasking Acoustics Classification algorithms Task analysis

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A Semisupervised Approach to Predicting a Twitch Streamer’s Growth based on the Game Streamed

A Semisupervised Approach to Predicting a Twitch Streamer’s...

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IEEE International Conference on Big Data

作者： Andrew Dybka Dominic Kocjan Samuel A. Ajila Dept. of Systems and Computer Engineering Carleton University Ottawa Canada

ISBN: (纸本)9781665480468

Games live streaming is growing rapidly as a form of entertainment. A game streamer will like to know what game to stream in order to attract huge number of viewers and followers which in turn will generate sizable income for the streamer. Using streamer’s metrics, the main goal of this research work is to design and develop a set of resources that a streamer can use to maximize the number of viewers and followers for a particular game and when to play the game. This research develops two models using machine learning techniques that can be used by game streamers to maximum the returns on investment. When both model predictions are presented as percentage, Model 1 using regression algorithms provides a MAE of 5.48 meaning the prediction has an error within 5.48% of the streamer’s total follower count. Also, Model 1 has 85.46% of its predictions’ absolute error less than or equal to 5. Similarly Model 2 with 2.53 MAE and 87.68% of its predictions’ absolute error less than or equal to 5.

关键词： Measurement Machine learning algorithms Entertainment industry Games Machine learning Forestry Predictive models

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A code-driven tutorial on encrypted control: From pioneering realizations to modern implementations

arXiv

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arXiv 2024年

作者： Schlüter, Nils Kim, Junsoo Darup, Moritz Schulze The Control and Cyberphysical Systems Group Dept. of Mechanical Engineering TU Dortmund University Germany The Department of Electrical and Information Engineering Seoul National University of Science and Technology Korea Republic of

The growing interconnectivity in control systems due to robust wireless communication and cloud usage paves the way for exciting new opportunities such as data-driven control and service-based decision-making. At the same time, connected systems are susceptible to cyberattacks and data leakages. Against this background, encrypted control aims to increase the security and safety of cyber-physical systems. A central goal is to ensure confidentiality of process data during networked controller evaluations, which is enabled by, e.g., homomorphic encryption. However, the integration of advanced cryptographic systems renders the design of encrypted controllers an interdisciplinary challenge. This code-driven tutorial paper aims to facilitate the access to encrypted control by providing exemplary realizations based on popular homomorphic cryptosystems. In particular, we discuss the encrypted implementation of state feedback and PI controllers using the Paillier, GSW, and CKKS cryptosystem. © 2024, CC BY-NC-ND.

关键词： Cryptography

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A Novel Hybrid Deep Learning Model for Detecting Breast Cancer

TechRxiv

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TechRxiv 2023年

作者： Banerjee, Avishek Chakraborty, Debasis Bhattacharyya, Basanti Nath, Ira Dey, Debashis Garg, Deepak Dept. of Computer Science and Business Systems Asansol Engineering College West Bengal 713305 India Dept. of Computer Science and Engineering Asansol Engineering College Asansol713305 India Dept. of Computer Science and Engineering Pailan College of Management and Technology West Bengal 700139 India Dept. of Computer Science and Engineering JIS College of Engineering West Bengal 741235 India Dept. of Computer Science and Engineering Maulana Abul Kalam Azad University of Technology West Bengal 700064 India School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India

Cancer is a deformity of the body cells that grow out of control and spread to other parts of body. According to the American Cancer Society, early identification of cancer resulted in a 99% chance of survival in the localized stage. In this article, a benchmark cancer dataset is considered for the purpose of investigation. After data preprocessing, we attempt to predict cancer type using our proposed deep learning model named as CNN-Soft-T that combines the feature selection (FS) and classification. FS is introduced using histogram technique for anomaly detection and dimension reduction. We have compared the performance of this model to other cutting-edge machine learning approaches including Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), Neural Network (NN) and Decision Tree (DT) in order to demonstrate its utility. We have experimentally exhibited the exceptional empirical effectiveness and biological applicability of our suggested hybrid model for cancer diagnostics and medication development. © 2023, CC BY.

关键词： Decision trees

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Dynamic Optimization of Provider-Based Scheduling for HPC Workloads

Dynamic Optimization of Provider-Based Scheduling for HPC Wo...

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International Conference on Software, Telecommunications and computer Networks (SoftCOM)

作者： Jacopo Marino Fulvio Risso Mauro Bighi Dept. of Control and Computer Engineering Politecnico di Torino Torino Italy Dept. of Information Technology PUNCH Torino S.p.A Torino Italy

The vast array of cloud providers present in today’s market proffer a suite of High-Performance Computing (HPC) services. However, these offerings are characterized by significant variations in execution times and cost structures. Consequently, selecting the optimal cloud provider and configuring the features of the chosen computing instance (e.g. virtual machines) proves to be a challenging task for users intending to execute HPC workloads. This paper introduces a novel component designed for effortless integration with existing HPC scheduling systems. This module’s primary function is to facilitate the selection of the most appropriate cloud provider for each distinct job, thereby empowering dynamic and adaptive cost-minimization strategies. Through the application of data augmentation techniques and the employment of Continuous Machine Learning, the system is endowed with the capability to operate efficiently with cloud providers that have not been previously utilized. Furthermore, it is capable of tracking the evolution of jobs over time. Our results show that this component can achieve consistent economic savings, based on the quality of the data used in the training phase.

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Forecasting Breakdowns of Electric Motors of a Sugar Factory Using Machine Learning Methods

Forecasting Breakdowns of Electric Motors of a Sugar Factory...

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Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Nataliia Zaiets Nataliia Lutska Lidiia Vlasenko Andrii Zhyltsov dept. of Automation and Robotic Systems National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine dept. of Automation and Computer Technologies of Control Systems National University of Food Technologies Kyiv Ukraine dept. of Software Engineering and Cyber Security State University of Trade and Economics Kyiv Ukraine dept. of electromagnetic systems The Institute of Electrodynamics of the National Academy of Sciences of Ukraine Kyiv Ukraine

the paper demonstrates the process of developing mathematical models for identifying breakdowns of electric motors using machine learning methods. The authors have developed three mathematical models for identifying breakdowns of electric drives with a power of 55 kW, 1500 rpm on the example of sugar production: model for quadratic discriminant analysis; binary classification decision tree; feed-forward neural network model. The best structures and parameters of the models were determined by machine learning methods. The performance of the models on the training and test sets was more than 96% by F-score. It was proposed to use all three models independently for diagnosing breakdowns of electric drives, while the decision maker makes the final decision on the replacement or repair of electrical equipment. This approach is justified and effective because of the diversity of models. The developed models, except for assessing the state of the engine, can also be used for simulation modelling, forecasting, and serve as information support for process operators in organizing equipment maintenance. The availability of real-time information about the state of equipment in production will allow timely repair or replacement of equipment, thereby reducing the risks of stopping production and increasing the resource efficiency of technological processes.

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Attenuation Resilient Underwater Ranging Using Petal-Like Structured Light with Tailorable Longitudinal Intensity

Attenuation Resilient Underwater Ranging Using Petal-Like St...

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2024 Laser Science, LS 2024

作者： Wang, Yingning Duan, Yuxiang Zeng, Ruoyu Zhou, Huibin Jiang, Zile Zuo, Yue Ramakrishnan, Muralekrishnan Bock, Robert Mojahedi, Mo Tur, Moshe Willner, Alan E. Dept. of Electrical Engineering University of Southern California Los AngelesCA90089 United States R-DEX Systems Inc. WoodstockGA30188 United States Department of Electrical and Computer Engineering University of Toronto TorontoONM5S 3G4 Canada School of Electrical Engineering Tel Aviv University Ramat Aviv 69978 Israel

We experimentally demonstrate a petal-like attenuation-resilient ranging beam with a ~9.5-dB central power enhancement at a 0.4-meter ranging distance, achieving 5 mm average ranging errors over 0-0.4 m in scattering ...

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DETReg Incorporating Semi-Supervised Learning for Object Detection in the Advanced Driver-Assistance systems

DETReg Incorporating Semi-Supervised Learning for Object Det...

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Robotics, engineering, Science, and Technology (RESTCON), International Conference on

作者： Keita Nakano Kousuke Matsushima Advanced Engineering School National Institute of Technology Kurume College Fukuoka Japan Dept. of Control and Information Systems Engineering National Institute of Technology Kurume College Fukuoka Japan

In Advanced Driver-Assistance systems (ADAS) and automatic driving, it is important to accurately recognize objects around the vehicle. DETReg is one of the unsupervised pre-training methods using Transformer, which is self-supervised by combining localization and categorization. DETReg performs self-supervised learning on unlabeled images. Then, it extracted a wide range of features from rich aspects of the data and gained the flexibility to adapt to many variations. Fine tuning then used the labeled dataset of the target task to fine tune the model to fit the specific dataset. This allowed DETReg to achieve higher accuracy in the object detection task. However, it is difficult to learn DETReg efficiently because of its slow learning time. In this paper, we propose a new pre-training method for object detection, called Semi-DETReg, that utilizes a few supervised labels during self-supervised learning. We incorporate semi-supervised learning into DETReg by using a portion of the supervised training data in the pre-training to improve efficiency. We demonstrate the effectiveness of our method by conducting experiments and comparing our method to a similarly trained DETReg.

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Analysis of Energy Potential of Micro Wind Turbines at Three Locations in the Central-Eastern Part of the Bulgarian Danube River Region

Analysis of Energy Potential of Micro Wind Turbines at Three...

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Communications, Information, Electronic and Energy systems (CIEES), International Conference on

作者： Nikolay Valov Katerina Gabrovska-Evstatieva Ivaylo Stoyanov Tsvetelina Kaneva Seher Kadirova Boris Evstatiev Nicolay Mihailov Dept. of Automation and Electronics University of Ruse "Angel Kanchev" Ruse Bulgaria Dept. of Computer Science University of Ruse "Angel Kanchev" Ruse Bulgaria Dept. of Electrical Power Engineering University of Ruse "Angel Kanchev" Ruse Bulgaria Dept. of Computer Systems and Technologies University of Ruse "Angel Kanchev" Ruse Bulgaria

ISBN: (数字)9798350352863

ISBN: (纸本)9798350352870

The share of renewable energy sources in the energy mix worldwide is gradually increasing. And even though they all have a highly variable nature, they can be forecasted statistically. The wind energy potential was investigated in this article in the Bulgarian Danube region, located between Ruse and Silistra. Two years of meteorological data, including the wind speed at 10 m height and the ambient temperature was used to estimate the average wind energy for each month of the year at three geographic locations – Ruse, Staro Selo (near Tutrakan), and Calarasi, Romania (near Silistra). The results showed that Staro Selo has the highest energy potential, reaching up to 0.75 kWh/day/m 2 in February, followed by Calarasi with 0.38 kWh/day/m 2 . The city of Ruse showed the lowest wind potential with the highest average monthly value reaching only 0.0027 kWh/day/m 2 . The obtained results showed that the regions around Tutrakan and Silistra are appropriate for the application of micro wind turbines.

关键词： Hands Temperature distribution Renewable energy sources Wind energy Wind speed Urban areas Wind turbines Rivers Wind forecasting Investment

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