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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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Optimal Multi-Target Path Planing control for Multiple Mobile Robot system

Optimal Multi-Target Path Planing Control for Multiple Mobil...

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2023 International Conference on system Science and engineering, ICSSE 2023

作者： Vu, Van-Phong Nguyen, Tu-Gia-Thinh Nguyen, Xuan-Sang Le, Van-Tung Ngo, Van-Thuyen Pham, Duc-Hung Uyen, Hoang Kim Ho Chi Minh City University of Technology and Education Department of Automatic Control Ho Chi Minh city Viet Nam Hung Yen University of Technology and Education Faculty of Electrical and Electronic Engineering Hung Yen Viet Nam Da Nang University of Science and Technology Faculty of Electronics and Telecommunication Engineering Da Nang Viet Nam

ISBN: (纸本)9798350322941

In this paper, the problems related to cooperative control for the multiple mobile robot system (MMRS) is presented. The LIDAR sensor is employed to obtain the 2D map of the indoor space. The formation control and the leader-following algorithm are applied to control the MMRS moving in a specific formation. Additionally, this paper considers the scenario that the MMRS has to move to several targets in the indoor space with many obstacles. Therefore, the indoor travelling salesman problem-based Ant Colony Optimization (ACO) is studied in this work to determine the optimal moving path of the MMRS. To prove the effectiveness and merit of the proposed methods, the simulation results are provided in this article. © 2023 IEEE.

关键词： Ant colony optimization

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Optimisation-Based Iterative Learning control for Distributed Consensus Tracking

Optimisation-Based Iterative Learning Control for Distribute...

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UKACC International Conference on control (control)

作者： Yueqing Zhang Bin Chen Bing Chu Zhan Shu Faculty of Engineering and Physical Sciences University of Southampton Southampton Department of Automatic Control and Systems Engineering University of Sheffield Sheffield U.K. Department of Electrical and Computer Engineering University of Alberta Edmonton Canada

ISBN: (数字)9798350374261

ISBN: (纸本)9798350374278

This paper addresses high-performance consensus tracking of repetitively operating networked dynamical systems using an iterative learning control (ILC) algorithm. It circumvents the need for precise model information in traditional methods and guarantees the high-performance by the predictive framework with a novel performance index that takes into account both current and future performance. The proposed algorithm ensures geometric convergence of the tracking error norm to zero and can be applied to both heterogeneous and non-minimum-phase systems. A distributed implementation of the algorithm is developed using the Alternating Direction Method of Multipliers, with detailed convergence analysis and numerical examples confirming its effectiveness.

关键词： system dynamics Heuristic algorithms Scholarships Predictive models Prediction algorithms Performance analysis Reliability

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Quantized Deep Neural Network Based Optimal control of Greenhouses on a Microcontroller

Quantized Deep Neural Network Based Optimal Control of Green...

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European control Conference (ECC)

作者： Kiran Kumar Sathyanarayanan Philipp Sauerteig Pablo Zometa Stefan Streif Automatic Control and System Dynamics Lab Technische Universität Chemnitz Chemnitz Germany Faculty of Engineering German International University Berlin Germany Department of Bioresources Fraunhofer Institute for Molecular Biology and Applied Ecology Giessen Germany

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

Growing crops in controlled-Environment Agriculture (CEA) farms, such as greenhouses and vertical farms, can help in meeting the demands of urban centers and achieving climate goals. Recently, many advanced control techniques like Model Predictive control (MPC) and its variants have been developed for energy-efficient operation and minimization of resource utilization. However, real-time implementation of these advanced strategies come along with certain computational hardware requirements, thus, increasing the operating costs. In this work, we propose to learn the MPC policy of a greenhouse control by means of a Deep Neural Network (DNN) in order to be implemented on a low-cost microcontroller. Additionally, we use a feedback law to reduce undesired quantization effects. The efficiency of our approach is exemplified by means of a simulation study for greenhouse control.

关键词： Quantization (signal) Microcontrollers Optimal control Greenhouses Artificial neural networks Minimization Real-time systems

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Noise Reduction in Industry Based on Virtual Instrumentation

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computers, Materials & Continua 2021年第10期69卷 1073-1096页

作者： Radek Martinek Rene Jaros Jan Baros Lukas Danys Aleksandra Kawala-Sterniuk Jan Nedoma Zdenek Machacek Jiri Koziorek Faculty of Electrical Engineering and Computer Science Department of Cybernetics and Biomedical EngineeringVSB–Technical University of Ostrava70800Ostrava-PorubaCzechia Faculty of Electrical Engineering Automatic Control and InformaticsOpole University of TechnologyOpolePoland Faculty of Electrical Engineering and Computer Science Department of TelecommunicationsVSB–Technical University of Ostrava70800Ostrava-PorubaCzechia

This paper discusses the reduction of background noise in an industrial environment to extend *** the Industry 4.0 era,the mass development of voice control(speech recognition)in various industrial applications is possible,especially as related to augmented reality(such as hands-free control via voice commands).As Industry 4.0 relies heavily on radiofrequency technologies,some brief insight into this problem is provided,including the Internet of things(IoT)and 5G *** study was carried out in cooperation with the industrial partner Brose CZ spol.s.r.o.,where sound recordings were made to produce a *** experimental environment comprised three workplaces with background noise above 100 dB,consisting of a laser/magnetic welder and a press.A virtual device was developed from a given dataset in order to test selected commands from a commercial speech recognizer from *** tested a hybrid algorithm for noise reduction and its impact on voice command recognition *** virtual devices,the study was carried out on large speakers with 20 participants(10 men and 10 women).The experiments included a large number of repetitions(100 times for each command under different noise conditions).Statistical results confirmed the efficiency of the tested *** welding environment efficiency was 27%before applied filtering,76%using the least mean square(LMS)algorithm,and 79%using LMS+independent component analysis(ICA).Magnetic welding environment efficiency was 24%before applied filtering,70%with LMS,and 75%with LMS+*** workplace environment efficiency showed no success before applied filtering,was 52%with LMS,and was 54%with LMS+ICA.

关键词： 5G hybrid algorithms signal processing speech recognition

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Biological Influence of Low-Frequency Electromagnetic Field Exposition Based on Selected Cell Death Markers

Biological Influence of Low-Frequency Electromagnetic Field ...

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ELEKTRO

作者： Robert Cichorowski Olga Kocikowska Marek Bajtoš Roman Radil Ladislav Janoušek Daria Gendosz De Carrillo Sebastian Student Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology liwice Poland Department of Engineering and Systems Biology Faculty of Automatic Control Electronics and Computer Science Gliwice Poland Department of Physiology Faculty of Medical Sciences Silesian University of Medicine Katowice Poland Department of Electromagnetic and Biomedical Engineering Faculty of Electrical Engineering and Informational Technology Žilina Slovakia Department of Histology Faculty of Medical Sciences in Zabrze Silesian University of Medicine Zabrze Poland Biotechnology Center Silesian University of Technology Gliwice Poland

ISBN: (数字)9798350372359

ISBN: (纸本)9798350372366

This work presents the results of the examination of the HeLa cell line exposure on the ELF-EMF (extremely low-frequency electromagnetic field). In particular, the relationship between ELF-EMF exposition time and cell death. The examination of cell death hallmarks were examined by estimation levels of selected proteins - FACL4 (a protein that is a part of the ferroptosis pathway) and CK18 (Cytokeratin related to necrosis and apoptosis pathways) in the proposed model of workers exposed to ELF-EMF week.

关键词： Proteins Biological system modeling Estimation Electromagnetic fields

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Handwritten Signature Recognition using Parallel CNNs and Transfer Learning for Forensics

Handwritten Signature Recognition using Parallel CNNs and Tr...

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

作者： Jaouhar Fattahi Feriel Sghaier Mohamed Mejri Ridha Ghayoula Emil Pricop Baha Eddine Lakdher Department of Computer Science and Software Engineering Laval University Quebec Canada Carthage National Engineering School Carthage University Tunis Tunisia Faculty of Engineering University of Moncton Canada Computers & Electronics Department Automatic Control Petroleum-Gas University of Ploieşti Ploieşti Romania Faculty of Science University of Monastir Monastir Tunisia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

Handwritten signatures hold paramount importance in legal, financial, and administrative domains, necessitating the development of robust signature recognition tools for forensic applications. This paper introduces a handwritten signature recognition (HSR) model employing Parallel Convolutional Neural Networks (CNN) tailored for forensic endeavors. Utilizing the parallel processing capabilities of CNN, our proposed approach adeptly analyzes and extracts discriminative features from handwritten signature images to facilitate precise recognition. In addition, we leverage several transfer learning techniques by parallelizing proven pre-trained CNNs. Extensive experimentation validates the efficacy of our approach on a standard dataset, demonstrating high accuracy and resilience in signature recognition tasks. The proposed approach exhibits substantial promise in augmenting forensic investigations by automating signature verification processes, thereby bolstering fraud detection efforts and upholding the integrity of legal documentation.

关键词： Deep learning Handwriting recognition Forensics Face recognition Transfer learning Feature extraction Fraud Convolutional neural networks Standards Resilience

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Delivering Improved Care through Remote Health Monitoring systems by Using Microservices. Case Study: Fall Detection Encompassed into Connected Care 24

Delivering Improved Care through Remote Health Monitoring Sy...

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24th International Conference on control systems and Computer Science, CSCS 2023

作者： Ianculescu, Marilena Alexandru, Adriana Giura, Ionut-Emanuel Vasilevschi, Ana-Mihaela National Institute for Research and Development in Informatics Communication Digital Applications and Systems Department Bucharest Romania University Politehnica of Bucharest Doctoral School of Automatic Control and Computers Bucharest Romania Counselors Department National Institute for Research and Development in Informatics Târgovişte Romania Faculty of Electrical Engineering Electronics and Information Technology Târgovişte Romania Valahia University of Târgovişte Târgovişte Romania

ISBN: (纸本)9798350313390

Based on today's modern technologies, patient care can be provided remotely, in a connected way, being, at the same time, personalized, patient-centered and proactive. Such an approach can be achieved using Remote Health Monitoring systems (RHMS). In case of elderly people, dramatic permanent deteriorations of their health status can be caused by falls. The seniors' health and life style monitoring and the detection and/or prevention of falls can be done by such RHMS, an example being the RO-SmartAgeing system developed inside 'Non-invasive Monitoring system and Health Assessment of the Elderly in a Smart Environment (RO-SmartAgeing)' research project. The paper presents the problem of using microservices for optimizing the architecture and functionalities of RO-SmartAgeing system, and the way in which the problems of ensuring the security of patient data can be solved. © 2023 IEEE.

关键词： Fall detection

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Multi-objective Optimization of Traffic Signal systems on Urban Arterial Roads 2nd

Multi-objective Optimization of Traffic Signal Systems on Ur...

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2nd International Symposium on Simulation and Process Modelling, ISSPM 2020

作者： Liu, Tao Qiao, Feng Guo, Lingzhong Chen, Yifan Faculty of Information and Control Engineering Shenyang Jianzhu University Shenyang China Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS13JD United Kingdom

ISBN: (纸本)9789813345744

In this paper, multi-objective optimization is used to solve the signal synchronization problem in arterial traffic roads, where a traffic dispersion module is introduced to further expand the solution space. By incorporating the models of delay time, queue length and stop times into the optimization, a first model called M1 is established. In the second model M2, the free flow speed assumption is replaced by a traffic dispersion module for better estimating the link travel time. A simulation study is then carried out on an arterial road, and the results show that the proposed strategy improves the performance of the traffic system compared to the current timing scheme and M2 has the best performance among all solutions in this paper, and the delay is reduced for about 24%. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Multiobjective optimization

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Trends and Challenges of Sustainable Agriculture in the Iraqi Environment

Trends and Challenges of Sustainable Agriculture in the Iraq...

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2022 IEEE International Conference on Blockchain, Smart Healthcare and Emerging Technologies, SmartBlock4Health 2022

作者： Al-Asadi, Hanan Hussein Goga, Maria Ali, Haider Abdullah Popa, Ramona Cristina Oludele, Awodele University POLITEHNICA of Bucharest Faculty of Automatic Control and Computers Bucharest Romania University of Technology-Iraq Baghdad Iraq Technical University of Civil Engineering of Bucharest UTCB Department for Training of Teaching Personnel Bucharest Romania Madenat Alelem University College Baghdad Iraq Babcock University Computer Science Department Ogun State Nigeria

ISBN: (纸本)9781665460149

The use of Agriculture Intelligent systems (AISs) in Iraq has become popular among farmers. The greenhouse is a solution for plant growth, which uses an effective microclimate to simulate seasons and to use alternative energy sources like solar, thermal, and photovoltaic systems for microclimate control. Greenhouses are considered a successful solution with developments and achievements in the technology of this field. However, problems like high costs, difficulties in installation and maintenance, or the need for some procedures should be considered. Because Iraq is close to the equator (3669 km), it receives sufficient solar power. It also has large areas available for the building of solar greenhouse systems. The farmers need an affordable and functional smart greenhouse to enable and encourage them to use the environment efficiently. We intend to develop low-cost, accurate, and easy-to-use greenhouse systems controlled by artificial intelligence algorithms. Farmers will be able to control the greenhouse by using simple commands. Machine learning algorithms provide real-time and effective analysis that could support farmers in decision-making and provide relevant data for researchers and policymakers. AISs algorithms can lead to a sustainable and productive agriculture system. The system can be used in Iraq with sufficient government support and subsidy. © 2022 IEEE.

关键词： Intelligent systems

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