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Predicting Drying Performance of Osmotically Treated Heat Sensitive Products Using Artificial Intelligence

computers, Materials & Continua 2021年第6期67卷 3143-3160页

作者： S.M.Atiqure Rahman Hegazy Rezk Mohammad Ali Abdelkareem M.Enamul Hoque Tariq Mahbub Sheikh Khaleduzzaman Shah Ahmed M.Nassef Department of Sustainable and Renewable Energy Engineering University of SharjahSharjahUAE College of Engineering at Wadi Addawaser Prince Sattam Bin Abdulaziz University11911Al-KharjSaudi Arabia Electrical Engineering Department Faculty of EngineeringMinia University61517MiniaEgypt Chemical Engineering Department Faculty of EngineeringMinia University61517MiniaEgypt Department of Biomedical Engineering Military Institute of Science and TechnologyDhakaBangladesh Department of Mechanical Engineering Military Institute of Science and TechnologyDhakaBangladesh Department of Infrastructure Engineering Melbourne School of EngineeringThe University of MelbourneAustralia Computers and Automatic Control Engineering Department Faculty of EngineeringTanta UniversityEgypt

The main goal of this research is to develop and apply a robust Artificial Neural Networks(ANNs)model for predicting the characteristics of the osmotically drying treated potato and apple samples as a model heat-sensitive product in vacuum contact *** salt and sugar solutions were used as the osmotic solutions at 27◦*** of experiments were performed at various temperatures of 35◦C,40◦C,and 55◦C for conduction heat input under vacuum(−760 mm Hg)*** experiments were also performed in a pure vacuum without heat *** moisture content(DMC),effective moisture diffusivity,and mass flux were considered as the performance parameters in this *** revealed that the osmotic dehydration using a concentrated sugar solution shows a higher reduction in the initial moisture loss of 19.87%compared to 5.3%in the salt ***,a significant enhancement of drying performance of about 27%in DMC was observed for both samples at vacuum and 40◦C compared to pure vacuum drying *** the experimental data,a robust artificial neural network(ANN)was proposed to describe the osmotic dehydration’s behavior on the drying *** ANN model outputs are the dimensionless moisture contents(DMC),the diffusivity,and the mass *** the ANN inputs were the drying time,the percent of sugar solution,and the percent of salt *** the ANN apple’s model,the minimum root mean square error(RMSE)values were 0.0261,0.0349 and 0.0406,for DMC,diffusivity,and mass flux,*** the best correlation coefficients of the above three parameters’determination values were 0.9909,0.9867 and 0.9744,*** the ANN potato’s model,the minimum RMSE values were 0.0124,0.0140 and 0.0333,for DMC,diffusivity,and mass flux,*** the best correlation coefficients of the parameters’values were found 0.9969,0.9968 and 0.9736,***,the ANN model’s prediction has a perfect agreement with th

关键词： Artificial neural network prediction modeling osmotic drying kinetics

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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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Wirelessly Actuated Rotation-free Magnetic Motor

Wirelessly Actuated Rotation-free Magnetic Motor

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Umur Ulas Harman Ahmed Hafez Cameron Duffield Zihan Zhao Luke Dixon Daniela Rus Shuhei Miyashita Automatic Control and Systems Engineering Department University of Sheffield Sheffield UK Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA Insigneo Institute for in Silico Medicine

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This paper addresses the challenge of actuating millimetre-sized motors, which are wirelessly driven by external magnetic fields. Traditional approaches, relying on rotating magnetic fields, often inadvertently cause the entire robot – especially if it is small and lightweight – to rotate, instead of a specified shaft in the motor. To overcome this issue, our study introduces a novel mechanism that leverages symmetrically configured magnetic motors to cancel out the torques, thus preventing unwanted rotation of the robot. This is achieved by utilizing a magnetic field along a single axis to induce rotational movement. The design features two millimetre-sized rotating magnets that interact to achieve a 90 ◦ rotation, complemented by an external magnetic field that accomplishes the remaining 270 ◦ , thus completing a full rotation. Furthermore, we demonstrate that applying a perpendicularly oriented magnetic field can inversely affect the motor’s rotation direction. A proof-of-concept experiment employing this mechanism successfully actuated a gripper in a water tank while it is free-floating, showcasing its potential for enhancing robotic applications at the sub-centimeter scale, where the small net torque of a miniature motor is essential.

关键词： Water Shafts Torque Gears Net zero Storage management Motors Permanent magnets Magnetic fields Grippers

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An improved side-slip estimation algorithm based on ultra-local model technique for autonomous vehicles

An improved side-slip estimation algorithm based on ultra-lo...

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

作者： Dániel Fényes Tamás Hegedűs Balázs Németh Vu Van Tan Péter Gáspár HUN-REN Institute for Computer Science and Control (SZTAKI) Budapest Hungary Department of Automotive Mechanical Engineering Faculty of Mechanical Engineering University of Transport and Communications Hanoi Vietnam

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

The paper presents a novel observer design method for estimating the front and rear wheel slips of the vehicle. The proposed observer design technique consists of two parts: A simple linear observer algorithm, which uses a reformulated lateral vehicle model to estimate the tire slips. The second part is based on an ultra-local model. The main goal of the ultra-local model is to eliminate the nonlinear, unmodeled, uncertain dynamics of the lateral vehicle model. In this way, the performance level of the linear observer can be significantly increased especially under critical circumstances such as high lateral acceleration maneuvers or driving on a low µ surface. The proposed observer algorithm is implemented in MATLAB/Simulink environment connected to the high-fidelity simulation software, CarMaker. The operation and the effectiveness of the proposed observer are demonstrated through several simulation examples.

关键词： Heuristic algorithms Software algorithms Wheels Observers Mathematical models Tires Software Vehicle dynamics MATLAB Information technology

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A Hybrid Temporal-Magnitude Based Sampling for Event-Based control

A Hybrid Temporal-Magnitude Based Sampling for Event-Based C...

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On-Line Conference (ONCON), Industrial Electronics Society Annual

作者： Oscar Miguel-Escrig José Sánchez-Moreno Julio-Ariel Romero-Pérez Sebastián Dormido-Bencomo Department of System Engineering and Design Universitat Jaume I Castelló de la Plana Spain Department of Computer Science and Automatic Control Universidad Nacional de Educación a Distancia (UNED) Madrid Spain

ISBN: (数字)9798331540319

ISBN: (纸本)9798331540326

In the current industrial environment, which is shifting towards an event-based control prone paradigm, sampling strategies in use must be revisited. Both temporal and magnitude dependent sampling strategies have been demonstrated to be suitable for control systems, reporting benefits in different categories. However, these sampling strategies also present some drawbacks, discussed in this contribution. Aiming to address some of these drawbacks, a new sampling strategy is proposed gathering characteristics of both predecessors.

关键词： Industrial electronics Transient response Quantization (signal) Event detection control systems Hybrid power systems Steady-state Data communication Guidelines

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BigFeat: Scalable and Interpretable Automated Feature engineering Framework

BigFeat: Scalable and Interpretable Automated Feature Engine...

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IEEE International Conference on Big Data

作者： Hassan Eldeeb Shota Amashukeli Radwa ElShawi Institute of Computer Science University of Tartu Tartu Estonia Computers and Automatic Control Department Faculty of Engineering Tanta University Tanta Egypt.

ISBN: (纸本)9781665480468

Feature engineering is a crucial step in building well-performing machine learning pipelines. However, manually constructing highly predictive features is time-consuming and requires domain knowledge. Although the research area of automated feature engineering has attracted much interest lately, both in academia and industry, the scalability and efficiency of the existing systems and tools are still practically unsatisfactory. This paper presents a scalable and interpretable automated feature engineering framework, BigFeat, that optimizes input features’ quality to maximize the predictive performance according to a user-defined metric. BigFeat employs a dynamic feature generation and selection mechanism that constructs a set of expressive features that improve the prediction performance while retaining interpretability. Extensive experiments are conducted, and the results show that BigFeat provides superior performance compared to the state-of-the-art automated feature engineering framework, AutoFeat, on a wide range of datasets. We show that BigFeat significantly improves the F1-Score of 8 classifiers by 4.59%, on average. In addition, the performance improvement achieved by integrating BigFeat into different AutoML frameworks is higher than that achieved by integrating AutoFeat into the same frameworks. Besides, the scalability of BigFeat is confirmed by its linear complexity, parallel design, and execution time which is, on average, 22x faster than AutoFeat.

关键词： Measurement Industries Scalability Pipelines Buildings Machine learning Big Data

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Enhancing Wastewater Treatment Sensor Fault Detection through Deep Learning

Enhancing Wastewater Treatment Sensor Fault Detection throug...

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

作者： Liliana-Maria Ghinea Mihaela Miron Marian Barbu Department of Automatic Control and Electrical Engineering University "Dunărea de Jos" Galați Romania Department of Computer Science and Information Technology University "Dunărea de Jos" Galați Romania

ISBN: (数字)9798350364293

ISBN: (纸本)9798350364309

Efficient operation and maintenance of wastewater treatment plants (WWTPs) are essential for safeguarding public health and the environment. The emergence of mechanical faults within complex systems can lead to disruptions, increased operational costs, and environmental risks. As the world moves towards a digitally connected and sustainable future, the development of Deep Learning (DL) tools for fault detection and isolation (FDI) in wastewater treatment processes is expected to become paramount. Therefore, in this study, we developed two neural models, a Feedforward Neural Network (FFNN) and a Long Short-Term Memory (LSTM), to address the detection of mechanical faults such as bias, stuck, spikes, and precision degradation of the Dissolved Oxygen (DO) sensor. The classification results showed remarkable accuracy performances during testing: for Dataset 1, FFNN achieved 96.56%, while LSTM reached 99.36%; and for Dataset 2, FFNN achieved 99.36%, and LSTM reached 99.57%.

关键词： Deep learning Degradation Accuracy Fault detection Plants (biology) Biological system modeling Data models Wastewater treatment Long short term memory Testing

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Using digital twins in health care

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer science 2021年第4期83卷 53-62页

作者： Boată, Adriana Angelescu, Radu Dobrescu, Radu Department of Automatic Control and Industrial Informatics Faculty of Automatic Control and Computer Science University POLITEHNICA of Bucharest Romania

This paper aims to summarize the progress made in the use of the Digital Twin (DT) concept for choosing the right drug for a person, as well as presenting a subnet model that predicts existing drugs for a particular pathology, based on genetic expressions involved in the disease, and genes addressed by therapy. Finally, as a result of the research, the paper discusses how the proposed method can be applied to search for a medication for the disease caused by the Covid 19 virus, with reference to the results of some clinical trials conducted after the onset of the pandemic. © 2021, Politechnica University of Bucharest. All rights reserved.

关键词： Gene expression

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On heterogeneity or sub-classes aspect in construction of stylometric input datasets

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Procedia computer science 2022年 207卷 2526-2535页

作者： Urszula Stańczyk Grzegorz Baron 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

Stylometric analysis of texts relies on learning characteristic traits of writing styles for authors. Once these patterns are discovered, they can be compared to the ones present in other text samples, to recognise their authorship. This recognition can be compromised if input datasets are prepared without taking into consideration possible stratification of the input space, leading to specific grouping of datapoints, or sub-classes within distinguished classes. The paper shows research dedicated to construction of various structures of input datasets, and combinations of such structures between train and test sets. In the research the influence of different stratification forms on the performance of selected popular classification systems was observed. To minimise the number of influencing factors, a task of authorship attribution was performed as binary classification with balanced classes. Stylometric descriptors exploited belonged to lexical and syntactic group, giving frequencies of occurrence for chosen style-markers. It resulted in real-valued attributes and these values were explored without applying discretisation, in order to avoid the possible bias of this procedure on observations.

关键词： Stylometry Dataset construction Heterogeneity Sub-classes Stratification Authorship attribution

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Machine Learning-Based Cascade Filtering System for fNIRS Data Analysis

Machine Learning-Based Cascade Filtering System for fNIRS Da...

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2023 Progress in Applied Electrical engineering, PAEE 2023

作者： Pelc, Mariusz Mikolajewski, Dariusz Ruotsalo, Tuukka Leiva, Luis A. Sudol, Adam Gorzelanczyk, Edward Jacek Lysiak, Adam Kawala-Sterniuk, Aleksandra Opole University of Technology Faculty of Electrical Engineering Automatic Control and Informatics Opole Poland Institute of Computer Science Kazimierz Wielki University Bydgoszcz Poland University of Helsinki Finland University of Copenhagen Denmark Faulty of Natural and Technical Sciences University of Opole Opole Poland University of Opole Faculty of Natural and Technical Sciences Opole Poland Institute of Philosophy Kazimierz Wielki University Bydgoszcz Poland

ISBN: (纸本)9798350316254

This paper presents a preliminary study on the use of machine learning-based methods to select the appropriate parameters of cascade filters in the analysis of brain signals recorded using functional infrared spectroscopy (fNIRS), which shows the level of oxygenation in the brain and, unlike EEG signals (showing electrical brain activity), are less prone to potential interference, disturbances or artifacts occurrence. © 2023 IEEE.

关键词： Machine learning

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