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advanced Spatio-Temporal Froth Analysis Using Smart Soft Sensors in Mineral Processing

SN computer science 2024年第4期5卷 363页

作者： Bendaouia, Ahmed Abdelwahed, El Hassan Qassimi, Sara Boussetta, Abdelmalek Benzakour, Intissar Amar, Oumkeltoum Chekroun, Mohamed Hasidi, Oussama Lachihab, Oussama Computer Systems Engineering Laboratory (LISI) Cadi Ayyad University Marrakech Morocco SEIA Department Moroccan Foundation for Advanced Science Innovation and Research (MAScIR) Rabat Morocco R & amp D and Engineering Center Reminex Managem Group Marrakech Morocco Computer and Systems Engineering Laboratory (L2IS) Cadi Ayyad University Marrakech Morocco

In the transformative field of mineral processing, the need for innovative technologies to overcome inherent difficulties and a critical shortage of high-quality data is an acute challenge. This study addresses these pressing issues by leveraging advanced spatio-temporal deep learning techniques, specifically Convolutional Long Short-Term Memory (ConvLSTM). Focused on the Zinc flotation circuit at CMG Managem Group in Morocco, our comprehensive approach encompasses meticulous data collection from a real-world industrial setting, rigorous spatial and temporal analyses, practical and accurate data augmentation, and the development of a ConvLSTM model for precise prediction of mineral grades. By capturing the temporal intricacies of froth behavior through video data, we gain insights into mineral concentration fluctuations, a critical aspect of traditional analysis. By offering a comprehensive solution that combines cutting-edge technology, meticulous data analysis, and practical augmentation methods, we aim to elevate not only mineral processing efficiency but also a broader spectrum of resource utilization and sustainable practices. This study offers a promising solution to elevate mineral processing efficiency and accuracy in the face of data limitations. It underscores the potential for transformative impacts on mineral processing operations, paving the way for more effective resource utilization and sustainable practices. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Data augmentation Industrial data analysis Machine learning Mineral processing

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Experimental observation of coherent-perfect-absorber and laser points in anti-PT symmetry

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Physical Review A 2024年第3期110卷 033504页

作者： Minye Yang Qi Zhong Zhilu Ye Şahin K. Özdemir Mohamed Farhat Ramy El-Ganainy Pai-Yen Chen State Key Laboratory for Manufacturing Systems Engineering School of Electronic Science and Engineering Department of Electrical and Computer Engineering Department of Engineering Science and Mechanics The Materials Research Institute Computer Electrical and Mathematical Science and Engineering Division Department of Physics Henes Center for Quantum Phenomena

The coexistence of coherent perfect absorber and laser or amplifier (CPAL) point is a peculiar spectral singularity associated with the scattering matrices of non-Hermitian systems. While the potential of CPAL systems for sensing application has been highlighted recently, the extreme sensitivity of parity-time (PT)-symmetric CPAL devices to the input signal deviations has so far impeded their practical utilization. Here we explore a strategy for implementing CPAL circuits by exploiting another type of non-Hermitian symmetry, namely anti-PT (APT) symmetry. We demonstrate that the condition for building CPAL in our proposed APT-symmetric electronic circuits additionally requires parity symmetry, which simplifies the circuit design and implementation. Additionally, we show that this newly proposed structure is 1.85 times more robust compared to previous CPAL devices studied in the literature.

关键词： Noise Quantum sensing Non-Hermitian systems PT-symmetry

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Staggered Microstrip Grating Structure for Sheet-Beam TWTs 25

Staggered Microstrip Grating Structure for Sheet-Beam TWTs

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25th Joint IEEE International Vacuum Electronics Conference and 15th International Vacuum Electron Sources Conference, IVEC + IVESC 2024

作者： Marosi, Robert Zuboraj, Muhammed Capolino, Filippo University of California Department of Electrical Engineering and Computer Science IrvineCA United States Los Alamos National Laboratory Los AlamosNM United States

ISBN: (纸本)9798350348705

We introduce a simple geometry for slow-wave structures (SWSs) in sheet-beam traveling-wave tubes (TWTs). The staggered microstrip grating SWS is a space harmonic structure with geometry and bandwidth with analogous to the staggered vane SWS commonly used for sheet beam TWTs. The staggered microstrip grating TWT is designed to operate in V-band with a center frequency of 74 GHz and electron beam voltage of 20 kV. © 2024 IEEE.

关键词： Slow wave structures

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Hemodynamic Analysis and Diagnosis Based on Multi-Deep Learning Models

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Fluid Dynamics & Materials Processing 2023年第6期19卷 1369-1383页

作者： Xing Deng Feipeng Da Haijian Shao School of Automation Key Laboratory of Measurement and Control for CSEMinistry of EducationSoutheast UniversityNanjing210096China School of Computer Jiangsu University of Science and TechnologyZhenjiang212003China Department of Electrical and Computer Engineering University of NevadaLas Vegas89154USA

This study employs nine distinct deep learning models to categorize 12,444 blood cell images and automatically extract from them relevant information with an accuracy that is beyond that achievable with traditional *** work is intended to improve current methods for the assessment of human health through measurement of the distribution of four types of blood cells,namely,eosinophils,neutrophils,monocytes,and lymphocytes,known for their relationship with human body damage,inflammatory regions,and organ illnesses,in particular,and with the health of the immune system and other hazards,such as cardiovascular disease or infections,more in *** results of the experiments show that the deep learning models can automatically extract features from the blood cell images and properly classify them with an accuracy of 98%,97%,and 89%,respectively,with regard to the training,verification,and testing of the corresponding datasets.

关键词： Blood cell analysis deep learning models classification-detection

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Ferrite Shielding for Eddy Current Suppression on Inductors and Transformers Using Ribbon-Based Magnetic Cores

Ferrite Shielding for Eddy Current Suppression on Inductors ...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Gao, Zihan Wang, Fred The University of Tennessee Knoxville Min H. Kao Department of Electrical Engineering and Computer Science KnoxvilleTN United States The University of Tennessee Min H. Kao Department of Electrical Engineering and Computer Science KnoxvilleTN United States Oak Ridge National Laboratory Oak RidgeTN United States

ISBN: (纸本)9798350376067

Ribbon-based magnetic cores, such as nanocrystalline and amorphous cores, have been widely used in inductors and transformers in high power converters. However, unwanted eddy current can be found on the surfaces of the core laminations due to fringing effect and leakage magnetic field. In this paper, a ferrite shielding structure is proposed to create bypass loops for lamination layers to avoid the stray field crossing the lamination layers. The mechanism of the proposed shielding is demonstrated. Simulations and tests have proved the effectiveness for the gapped inductor, as well as core and shell-type transformers. © 2024 IEEE.

关键词： Magnetic leakage

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A novel approach to enhance feature selection using linearity assessment with ordinary least squares regression for Alzheimer’s Disease stage classification

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Multimedia Tools and Applications 2024年第38期83卷 86059-86078页

作者： Mabrouk, Besma Bouattour, Nadia Mabrouki, Noura Sellami, Lamia Ben Hamida, Ahmed Advanced Technologies for Medicine and Signals ATMS Department of Electrical and Computer Engineering National Engineers School Sfax Tunisia Department of Neurology University Hospital Habib Bourguiba Sfax Tunisia Department of Psychiatry University Hospital Taher Sfar Mahdia Tunisia Department IS College of Computer Science King Khaled Universiy ‘KKU’ Abha Kingdom of Saudi Arabia Saudi Arabia

Diagnosing Alzheimer’s disease (AD) in its prodromal stage is a significantly crucial area of research. Approximately 50% of individuals within the well-known Mild Cognitive Impairment (MCI) cohort are estimated to progress to AD, and the factors influencing conversion remain unknown. Gaining insights into the disease evolution can enhance support strategies and potentially slow down the pathology. Utilizing the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, our objective is to construct a framework for distinguishing between Normal Controls (NC) and different stages of Alzheimer’s Disease (AD), encompassing Earlier Mild Cognitive Impairment (EMCI), Later Mild Cognitive Impairment (LMCI), and AD patients. In pursuit of this objective, we preprocessed Diffusion Tensor and Magnetic Resonance brain images from 237 subjects, generating corresponding brain connectivity maps. Notably, we introduce an innovative linearity assessment method that utilizes the Ordinary Least Squares (OLS) linear regression model to identify and select relevant features for classification. This approach effectively identifies features with strong linear relationships to the target variable. Our method’s superiority is demonstrated through a comparative analysis with the traditional SelectKBest approach. By integrating this feature selection strategy with a Logistic Regression model, our study achieves both efficient and highly accurate classification outcomes, highlighting the effectiveness of the proposed method. In a four-class classification scenario, the model attained an accuracy of 66%±0.06. In binary classification, the results were equally impressive, with an area under the curve of 0.68±0.10% for CN vs. EMCI discrimination, 99±0.02%for distinguishing LMCI from adjacent classes CN and EMCI, and 0.79%±0.08 for discriminating AD from healthy subjects. Additionally, the calculation of Pearson’s correlation coefficient has been employed to identify cortical regions affected b

关键词： Neurodegenerative diseases

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Color Image Steganalysis Algorithm Based on Quaternion Convolutional Neural Network 30th

Color Image Steganalysis Algorithm Based on Quaternion Convo...

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30th International Conference on Computational and Experimental engineering and sciences, ICCES 2024

作者： Xu, Meng Wang, Jinwei State Key Laboratory of Mathematical Engineering and Advanced Computing Henan450001 China Department of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China

ISBN: (纸本)9783031687747

Most of the images on the Internet are color images, and steganalysis of color images is a very critical issue in the field of steganalysis. The current proposed color image steganalysis features mainly rely on manual design, and the steganalysis features do not fully consider the internal connections between the three channels of color images. Most steganalyzers based on deep learning are also designed for grayscale images. In recent years, the continuous introduction of advanced steganography techniques for color images has also brought more severe challenges to the steganography analysis of color images. Quaternions are a tool that can effectively represent color images, and utilizing quaternion transformations can fully exploit the correlation between color image channels. Combining quaternion theory with convolutional neural networks, this paper proposed a color image steganalysis algorithm based on quaternion convolutional neural networks. Firstly, the image is represented by quaternion, and then input into the quaternion convolutional neural network. The convolution kernel in the network is composed of quaternions, and then perform convolution and pooling. The experimental results have demonstrated the significant effectiveness of the proposed network. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks

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Highly Efficient Dual-Buck Structured Buck-Boost AC-AC Converter With Versatile Identical Inverting/Noninverting Operations

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IEEE Transactions on Industrial Informatics 2023年第12期19卷 11403-11417页

作者： Ahmed, Hafiz Furqan Alzaabi, Omar El Moursi, Mohamed Shawky Al Hosani, Khalifa National Sun Yat-sen University Department of Electrical Engineering Kaohsiung804201 Taiwan Khalifa University Advanced Power and Energy Center Department of Electrical Engineering and Computer Science Abu Dhabi127788 United Arab Emirates Mansoura University Faculty of Engineering Mansoura35516 Egypt

Single-phase dual-buck ac-ac (DBAC) converters are gaining attention due to their intrinsic protection from shoot-through and open-circuit problems of conventional ac-ac converters. However, research works on DBAC converters are mainly concentrated around unipolar topologies. In a few developed bipolar topologies to date, the inverting buck-boost operations (for series voltage injection and step-variable frequency outputs) are inefficient and burdened by large voltage and current stresses (v in + vo and i in + i o) of switching devices and ripples of passive elements. In this article, an efficient dual-buck structured buck-boost ac-ac converter is proposed, with the following features: no voltage source shoot-through and inductor open-circuit problems, natural attainment of safe commutation without additional protection circuitry or complex control, no need for pulsewidth modulation dead times, and elimination of high-frequency conduction of mosfet's body diodes and related slow reverse recovery issues. The proposed converter provides distinct types of efficient inverting and noninverting buck and boost operations, with smaller switch voltage/current stresses (v in/vo and i in/i o) and passive component ripples. Combined inverting and noninverting buck-boost operations are also proposed with separate tuning of buck and boost voltage transfer ratios. A simple adaptable switching strategy provides the switch control pulses for all circuit operations by modulating the buck and boost control reference signals. The proposed converter provides sustained input/output currents and performs well with nonresistive loads. Extensive theoretical analysis is presented followed by practical verifications on a 400-VA laboratory circuit. © 2005-2012 IEEE.

关键词： Buck-Boost converter

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Frequency Domain Auto-tuning of Structured LPV Controllers for High-Precision Motion Control 63

Frequency Domain Auto-tuning of Structured LPV Controllers f...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Broens, Yorick Butler, Hans Toth, Roland Asml Veldhoven Netherlands Eindhoven University of Technology Department of Electrical Engineering Eindhoven Netherlands Hunren Institute for Computer Science and Control Systems and Control Laboratory Hungary

ISBN: (纸本)9798350316339

Motion systems are a vital part of many industrial processes. However, meeting the increasingly stringent demands of these systems, especially concerning precision and throughput, requires novel control design methods that can go beyond the capabilities of traditional solutions. Traditional control methods often struggle with the complexity and position-dependent effects inherent in modern motion systems, leading to compromises in performance and a laborious task of controller design. This paper addresses these challenges by introducing a novel structured feedback control auto-tuning approach for multiple-input multiple-output (MIMO) motion systems. By leveraging frequency response function (FRF) estimates and the linear-parameter-varying (LPV) control framework, the proposed approach automates the controller design, while providing local stability and performance guarantees. Key innovations include norm-based magnitude optimization of the sensitivity functions, an automated stability check through a novel extended factorized Nyquist criterion, a modular structured MIMO LPV controller parameterization, and a controller discretization approach which preserves the continuous-time (CT) controller parameterization. The proposed approach is validated through experiments using a state-of-the-art moving-magnet planar actuator prototype. © 2024 IEEE.

关键词： Continuous time systems

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Data Security Defense: Modeling and Detection of Synchrophasor Data Spoofing Attack for Grid Edge

Data Security Defense: Modeling and Detection of Synchrophas...

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2024 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2024

作者： Qiu, Wei Yin, He Wu, Yuru Zeng, Chujie Chen, Chang Dong, Yuqing Liu, Yilu The University of Tennessee Department of Electrical Engineering & Computer Science KnoxvilleTN United States Oak Ridge National Laboratory ORNL KnoxvilleTN United States

ISBN: (纸本)9798350313604

Data security and cyberattack have become critical issues in the distributed power system where adversaries can swap the source information of sensors or even spoof and alter measurements. However, the cyber security of the power system is challenged by the unpredictability and stealth of the spoofing attacks. To protect the data security at the grid edge, this paper developed a synchrophasor data spoofing attack detection framework based on the time-frequency feature extraction techniques including the short-Time Fourier transform (STFT) and object detection network for real-Time synchrophasor data categorization and spoofing attack localization. The proposed approach outperforms earlier work in terms of spoofing attack detection and offers a vital localization function employing distributed synchrophasor sensors. © 2024 IEEE.

关键词： Frequency domain analysis

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