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15th National Conference of Operational and Systems Research, BOS 2018 and 17th International Workshop on Intuitionistic Fuzzy Sets and General Nets, IWIFSGN 2018

作者： Przybyla, Tomasz Pander, Tomasz Institute of Electronics Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology 16 Akademicka St. Gliwice44-100 Poland

ISBN: (纸本)9783030470234

In this paper we propose a modification of the clustering based nonlinear state–space projection (CNPF) method. The whole filtering process takes place in a reconstructed state–space by applying the time–delay embedding technique. Then, each state-space point is projected on the constructed signal subspace. The subspace has a constant dimension and is the same for all constructed subspaces. A modification is introduced in such a way that subspace dimension may differ depending on the currently processed part of the signal. For this purpose we introduced a concept of signal’s eigenportrait. The proposed method is applied to process different ECG signals contaminated with a real electromyographic (EMG) noise. As the reference, the CNPF method is used. For different noise levels, the proposed method appears more effective than the reference one. © 2021, Springer Nature Switzerland AG.

关键词： Principal component analysis

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Balancing objects on DOORS nodes 23

Balancing objects on DOORS nodes

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23rd International Conference on control Systems and computer science Technologies, CSCS 2021

作者： Palanciuc, Dorin Pop, Florin University Politehnica of Bucharest Faculty of Automatic Control and Computers Computer Science Department Romania National Institute for Research and Development in Informatics ICI Bucharest Romania

ISBN: (纸本)9781665439398

In the context of DOORS, a medium-scale distributed system, running on tens of 'normal PCs and/or embedded devices, we propose a solution for the problem of efficient allocation of execution and storage resources. We distinguish between the node-level problem of scheduling the execution of object methods and the system-level problem of optimally placing objects on nodes such that locality is maximised: inter-communicating objects are kept as close to each other as possible and the most 'active' objects are hosted on nodes with the highest processing capacity and/or the highest amount of memory available. DOORS aims for architectural simplicity and achieves this by providing more control to the solution implementer, such that the behaviour of the object balancer can be tailored to the specifics of the problem addressed by the implementation. The proposed design for the balancer provides a simple relocation service, and a set of perpetually updated set of metrics, such that the solution implementer may define, 'plug in' and enforce a balancing policy out of an unlimited number of alternatives. © 2021 IEEE.

关键词： Balancing

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On the Impact of Noisy Labels on Supervised Classification Models 23rd

On the Impact of Noisy Labels on Supervised Classificatio...

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23rd International Conference on Computational science, ICCS 2023

作者： Dubel, Rafal Wijata, Agata M. Nalepa, Jakub Faculty of Automatic Control Electronics and Computer Science Department of Algorithmics and Software Silesian University of Technology Akademicka 16 Gliwice44-100 Poland Faculty of Biomedical Engineering Silesian University of Technology Roosevelta 40 Zabrze41-800 Poland KP Labs Konarskiego 18C Gliwice44-100 Poland

ISBN: (纸本)9783031360206

The amount of data generated daily grows tremendously in virtually all domains of science and industry, and its efficient storage, processing and analysis pose significant practical challenges nowadays. To automate the process of extracting useful insights from raw data, numerous supervised machine learning algorithms have been researched so far. They benefit from annotated training sets which are fed to the training routine which elaborates a model that is further deployed for a specific task. The process of capturing real-world data may lead to acquring noisy observations, ultimately affecting the models trained from such data. The impact of the label noise is, however, under-researched, and the robustness of classic learners against such noise remains unclear. We tackle this research gap and not only thoroughly investigate the classification capabilities of an array of widely-adopted machine learning models over a variety of contamination scenarios, but also suggest new metrics that could be utilized to quantify such models’ robustness. Our extensive computational experiments shed more light on the impact of training set contamination on the operational behavior of supervised learners. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Digital storage

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Deciphering Modern Customer Loyalty: A Machine Learning Approach

Deciphering Modern Customer Loyalty: A Machine Learning Appr...

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International Conference on Internet of Things and Machine Learning, IoTML 2023

作者： Li, Kechen Xu, Chang Zhao, Zheyuan Zhu, Mengyu Cui, Xiangxuan Xu, Shurui Zou, Jichen Nantong Normal College Nantong China Ulink College of Suzhou industrial Park Suzhou China Jiangsu Lincheng Technology Co. Ltd. Nantong China School of computer science and technology China University of Mining and Technology Xuzhou China Department of Automatic Control and Systems Engineering The University of Sheffield Sheffield United Kingdom

ISBN: (纸本)9781510671805

In the contemporary retail sector, deciphering customer behavior is crucial for businesses vying for a competitive advantage. While customer loyalty has conventionally been gauged through parameters such as repeat purchases and promotional response, the merging of technology, media, and commerce has necessitated a reevaluation of these benchmarks. This study recognizes the inadequacy of solely relying on traditional transactional data to evaluate loyalty. Utilizing Machine Learning (ML), we crafted a novel model to predict customer loyalty, taking into account a plethora of modern factors, including online behavior and social media interactions. This research leveraged a diverse dataset and sophisticated ML algorithms to identify subtle patterns and relationships. Key findings include the significance of online behavior in loyalty prediction, the emerging role of social media interactions, and the nuanced influence of device preferences on brand loyalty. Notably, this manuscript offers a pioneering ML model that accurately reflects the complexities of the modern retail landscape, providing a valuable tool for businesses seeking to foster enduring customer relationships. © 2023 SPIE.

关键词： Forecasting

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Possible Use of Fuzzy Sets Similarity for Patient’s Parameters Observation 15th

Possible Use of Fuzzy Sets Similarity for Patient’s Paramet...

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15th National Conference of Operational and Systems Research, BOS 2018 and 17th International Workshop on Intuitionistic Fuzzy Sets and General Nets, IWIFSGN 2018

作者： Straszecka, Ewa Pander, Tomasz Institute of Electronics Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology 16 Akademicka St. Gliwice44-100 Poland

ISBN: (纸本)9783030470234

Nowadays medical equipment makes possible performing tests by patients themselves. These tests are usually health parameter measurements, like a glucose level or blood pressure. Though they are rather regularly performed, they are hardly used in patient’s state monitoring. These measurements are not suitable for interpretation by means of time series as they include few values and they are strongly influenced by actual living conditions or habits. The paper suggests an interpretation of series of measurements by means of fuzzy sets and next using a similarity measure of membership functions to disclose tendencies in the parameters’ change. It is shown for data available in the Internet that the proposed method can be used even for series of few measurements and that information extracted in such a way is qualitatively different from the classical method of moving average. The method can be used for multi-criteria self-monitoring of a patient, in future. © 2021, Springer Nature Switzerland AG.

关键词： Fuzzy sets

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A CNN Approach To Predicting Non-Invasive Blood Pressure of Surgical Patients 5

A CNN Approach To Predicting Non-Invasive Blood Pressure of ...

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5th IEEE International Conference on Knowledge Innovation and Invention, ICKII 2022

作者： Ko, Pin-You Lai, Lien-Fu Ku, Tien-Hsiung Lin, Yue-Der National Changhua University of Education Department of Computer Science and Information Engineering Changhua Taiwan Changhua Christian Hospital Ai Development Center Department of Anesthesiology Changhua Taiwan Feng Chia University Department of Automatic Control Engineering Taichung Taiwan

ISBN: (数字)9781665479295

ISBN: (纸本)9781665479295

The occurrence of intraoperative hypertension/ hypotension may cause danger to a patient. Therefore, the monitoring of blood pressure change during surgical operation is momentous for anesthetized patient undergoing surgical procedures. The invasive method of measuring arterial blood pressure (ABP) provides accurate and much information, but it requires evaluation by an anesthesiologist and with its long-term use, it also brings a higher risk of infection, and even some patients have pain and discomfort at the catheter placement site after surgery, so it is not suitable for all types of patients. On the other hand, non-invasive method of measuring cuff blood pressure provides little information because of non-continuous measurement. With the emerging of deep learning, there has been more and more discussion on whether non-invasive Photoplethysmography (PPG) can replace invasive ABP as an alternative to continuous BP measurement and even BP prediction. This paper aims to implement a deep learning model to predict SBP/DBP signal by PPG, PPG extended data and Electrocardiography (ECG) data. We trained the CNN-based architecture on preprocessed data including ECG, PPG, 1st and 2nd derivative PPG signal extracted from MIMIC-III(Medical Information Mart for Intensive Care III) waveform database matched subset to predict continuous ABP signals using real world data. The value of SBP and DBP are directly predicted to effectively estimate the accuracy of the predictions. The prediction results fulfill the grade A in British Hypertension Society (BHS) standard and most part of Association for the Advancement of Medical Instrumentation (AAMI)'s standard. The proposed model has been applied for CHANGHUA CHRISTIAN HOSPITAL's IRB NO:191240. The results depict that our proposed model could effectively predict SBP/DBP signal and be deployed to the operating room realistically. © 2022 IEEE.

关键词： Blood pressure

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Trust region policy optimization with optimal transport discrepancies: duality and algorithm for continuous actions 22

Trust region policy optimization with optimal transport disc...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Antonio Terpin Nicolas Lanzetti Batuhan Yardim Florian Dörfler Giorgia Ramponi Automatic Control Laboratory ETH Zürich Dept. of Computer Science ETH Zürich Dept. of Computer Science ETH AI Center

ISBN: (纸本)9781713871088

Policy Optimization (PO) algorithms have been proven particularly suited to handle the high-dimensionality of real-world continuous control tasks. In this context, Trust Region Policy Optimization methods represent a popular approach to stabilize the policy updates. These usually rely on the Kullback-Leibler (KL) divergence to limit the change in the policy. The Wasserstein distance represents a natural alternative, in place of the KL divergence, to define trust regions or to regularize the objective function. However, state-of-the-art works either resort to its approximations or do not provide an algorithm for continuous state-action spaces, reducing the applicability of the method. In this paper, we explore optimal transport discrepancies (which include the Wasserstein distance) to define trust regions, and we propose a novel algorithm - Optimal Transport Trust Region Policy Optimization (OT-TRPO) - for continuous state-action spaces. We circumvent the infinite-dimensional optimization problem for PO by providing a one-dimensional dual reformulation for which strong duality holds. We then analytically derive the optimal policy update given the solution of the dual problem. This way, we bypass the computation of optimal transport costs and of optimal transport maps, which we implicitly characterize by solving the dual formulation. Finally, we provide an experimental evaluation of our approach across various control tasks. Our results show that optimal transport discrepancies can offer an advantage over state-of-the-art approaches.

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Efficient Detection of Invasive Stink Bugs Using Convolutional Neural Networks

Efficient Detection of Invasive Stink Bugs Using Convolution...

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Telecommunications Forum (TELFOR)

作者： Alexandru Dinca Nicoleta Angelescu Loretta Ichim Trandafir-Liviu Serghei Dan Popescu Faculty of Automatic Control and Computer Science National University of Science and Technology Politehnica Bucharest Bucharest Romania Faculty of Electrical Engineering Electronics and Information Technology Valahia University of Targoviste Targoviste Romania

Timely pest detection and identification is critical as part of modern agriculture. Halyomorpha Halys is a prevalent pest with proven harmful impacts on numerous crops and agricultural regions. The paper proposes an efficient model to improve the detection of two invasive stink bugs: Halyomorpha halys and Nezara Viridula. automatic detection of these two bugs is essential in various fields, such as precision agriculture and integrated pest management. The high performances obtained in the present study open new perspectives for the further development of insect pest detection systems and can serve as a basis for future modifications and improvements of these models.

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Evaluation of Differentially Constrained Motion Models for Graph-Based Trajectory Prediction

Evaluation of Differentially Constrained Motion Models for G...

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IEEE Symposium on Intelligent Vehicle

作者： Theodor Westny Joel Oskarsson Björn Olofsson Erik Frisk Department of Electrical Engineering Linköping University Sweden Department of Computer and Information Science Linköping University Sweden Department of Automatic Control Lund University Sweden

Given their flexibility and encouraging performance, deep-learning models are becoming standard for motion prediction in autonomous driving. However, with great flexibility comes a lack of interpretability and possible violations of physical constraints. Accompanying these data-driven methods with differentially-constrained motion models to provide physically feasible trajectories is a promising future direction. The foundation for this work is a previously introduced graph-neural-network-based model, MTP-GO. The neural network learns to compute the inputs to an underlying motion model to provide physically feasible trajectories. This research investigates the performance of various motion models in combination with numerical solvers for the prediction task. The study shows that simpler models, such as low-order integrator models, are preferred over more complex, e.g., kinematic models, to achieve accurate predictions. Further, the numerical solver can have a substantial impact on performance, advising against commonly used first-order methods like Euler forward. Instead, a second-order method like Heun’s can greatly improve predictions.

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MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction with Neural ODEs

arXiv

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arXiv 2023年

作者： Westny, Theodor Oskarsson, Joel Olofsson, Björn Frisk, Erik Department of Electrical Engineering Linköping University Sweden Department of Computer and Information Science Linköping University Sweden Department of Automatic Control Lund University Sweden

Enabling resilient autonomous motion planning requires robust predictions of surrounding road users' future behavior. In response to this need and the associated challenges, we introduce our model titled MTP-GO. The model encodes the scene using temporal graph neural networks to produce the inputs to an underlying motion model. The motion model is implemented using neural ordinary differential equations where the state-transition functions are learned with the rest of the model. Multimodal probabilistic predictions are obtained by combining the concept of mixture density networks and Kalman filtering. The results illustrate the predictive capabilities of the proposed model across various data sets, outperforming several state-of-the-art methods on a number of metrics. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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