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Industrial Automation, Robotics and Control engineering (IARCE), International Conference on

作者： Mingyi Sun Weigang Cui Yang Li Department of Automation Science and Electrical Engineering Beihang University Beijing China School of Engineering Medicine Beihang University Beijing China Department of Automation Science and Electrical Engineering State Key Laboratory of Virtual Reality Technology and Systems The Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

Electroencephalography (EEG) has emerged as a crucial cornerstone within the realm of brain-computer interface (BCI) applications, with its significance notably pronounced in the field of fatigue detection. However, the inherent limitations of EEG acquisition equipment during real driving scenarios have contributed to the constrained robustness of existing models. Moreover, most of recent methods failed to extract robust multi-domain features, leading to a suboptimal performance. To address these challenges, we propose a novel channel-augmented multi-domain graph convolutional network (CA-MDGCNet). Specifically, the initial EEG signals are enriched by incorporating supplementary virtual EEG channels, distinguished as learnable parameters within the network architecture. Then, differential entropy features are extracted from the augmented EEG signals. Following this, a multi-domain graph convolutional network is designed to encode high-level EEG features by means of convolutions in diverse paths, which is beneficial to integrating the characteristics extracted from multiple domains. Finally, the classification block derives the detection outcome from the refined feature maps. To substantiate the potency of the proposed method, the validation was conducted on the publicly accessible SEED-VIG. The proposed CA-MDGCNet not only demonstrates more promising performance compared to state-of-the-art approaches but also underscores the potential viability of our method for the realm of fatigue driving detection.

关键词： Graph convolutional networks Convolution Service robots Neural activity Feature extraction Fatigue Brain modeling Electroencephalography Entropy Robustness

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Extracting GHZ states from linear cluster states

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Physical Review Research 2024年第1期6卷 013330-013330页

作者： J. de Jong F. Hahn N. Tcholtchev M. Hauswirth A. Pappa Electrical Engineering and Computer Science Department Technische Universität Berlin 10587 Berlin Germany Dahlem Center for Complex Quantum Systems Freie Universität Berlin 14195 Berlin Germany Fraunhofer Institute for Open Communication Systems - FOKUS 10589 Berlin Germany

Quantum information processing architectures typically only allow for nearest-neighbor entanglement creation. In many cases, this prevents the direct generation of GHZ states, which are commonly used for many communication and computation tasks. Here, we show how to obtain GHZ states between nodes in a network that are connected in a straight line, naturally allowing them to initially share linear cluster states. We prove a strict upper bound of ⌊(n+3)/2⌋ on the size of the set of nodes sharing a GHZ state that can be obtained from a linear cluster state of n qubits, using local Clifford unitaries, local Pauli measurements, and classical communication. Furthermore, we completely characterize all selections of nodes below this threshold that can share a GHZ state obtained within this setting. Finally, we demonstrate these transformations on the IBMQ Montreal quantum device for linear cluster states of up to n=19 qubits.

关键词： Measurement-based quantum computing Quantum communication Quantum entanglement Quantum networks Superconducting qubits

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Industry 4.0: Architecture and Equipment Revolution

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Computers, Materials & Continua 2021年第2期66卷 1175-1194页

作者： Ahmed Bashar Fakhri Saleem Latteef Mohammed Imran Khan Ali Safaa Sadiq Basem Alkazemi Prashant Pillai Bong Jun Choi Department of Medical Instrumentation Techniques Engineering Electrical Engineering Technical CollegeMiddle Technical UniversityBaghdadIraq Department of Electrical Engineering University of Engineering and Technology PeshawarPeshawarPakistan School of Mathematics and Computer Science University of WolverhemptonWulfruna StreetWolverhemptonUK Center of Artificial Intelligence Research and Optimization Torrens UniversityBrisbaneAustralia Department of Computer Science College of Computer&Information SystemsUmm Al-Qura UniversityMeccaSaudi Arabia School of Computer Science and Engineering Soongsil UniversitySeoulSouth Korea

The development of science and technology has led to the era of Industry *** core concept is the combination of“material and informationization”.In the supply chain and manufacturing process,the“material”of the physical entity world is realized by data,identity,intelligence,and *** 4.0 is a disruptive transformation and upgrade of intelligent industrialization based on the Internet-of-Things and Big Data in traditional *** goal is“maximizing production efficiency,minimizing production costs,and maximizing the individual needs of human beings for products and services.”Achieving this goal will surely bring about a major leap in the history of the industry,which will lead to the“Fourth Industrial Revolution.”This paper presents a detailed discussion of industrial big data,strategic roles,architectures,characteristics,and four types of innovative business models that can generate profits for *** key revolutionary aspect of Industry 4.0 is explained,which is the equipment *** important attributes of equipment are explained under the Industry 4.0 perspective.

关键词： Industry 4.0 big data smart manufacturing business model equipment revolution

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An Optimized Ensemble Model for Prediction the Bandwidth of Metamaterial Antenna

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Computers, Materials & Continua 2022年第4期71卷 199-213页

作者： Abdelhameed Ibrahim Hattan F.Abutarboush Ali Wagdy Mohamed Mohamad Fouad El-Sayed M.El-kenawy Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Electrical Engineering Department College of EngineeringTaibah UniversityMedina42353Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGizaEgypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyEgypt

Metamaterial Antenna is a special class of antennas that uses metamaterial to enhance their *** size affects the quality factor and the radiation loss of the *** antennas can overcome the limitation of bandwidth for small *** learning(ML)model is recently applied to predict antenna *** can be used as an alternative approach to the trial-and-error process of finding proper parameters of the simulated *** accuracy of the prediction depends mainly on the selected *** models combine two or more base models to produce a better-enhanced *** this paper,a weighted average ensemble model is proposed to predict the bandwidth of the Metamaterial *** base models are used namely:Multilayer Perceptron(MLP)and Support Vector Machines(SVM).To calculate the weights for each model,an optimization algorithm is used to find the optimal weights of the *** Group-Based Cooperative Optimizer(DGCO)is employed to search for optimal weight for the base *** proposed model is compared with three based models and the average ensemble *** results show that the proposed model is better than other models and can predict antenna bandwidth efficiently.

关键词： Metamaterial antenna machine learning ensemble model multilayer perceptron support vector machines

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Resilience of Data-Driven Cyberattack Detection systems in Smart Power Grids

Resilience of Data-Driven Cyberattack Detection Systems in S...

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European Signal Processing Conference (EUSIPCO)

作者： Abdulrahman Takiddin Rachad Atat Hayat Mbayed Muhammad Ismail Erchin Serpedin Department of Electrical and Computer Engineering Florida State University Tallahassee FL USA Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar Center for Advanced Power Systems Florida State University Tallahassee FL USA Department of Computer Science Tennessee Technological University Cookeville TN USA Department of Electrical and Computer Engineering Texas A&M University College Station TX USA

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Data communications within the smart power grid components are susceptible to cyberattacks due to the inter-connected nature of the grid and reliance on communication networks. Such cyberattacks can exploit the integrity of the exchanged data and result in operational instability. Existing data-driven cyberattack detection systems (CDSs) are proposed in the literature but their effectiveness is only verified against one type of cyberattacks. In reality, a smart grid system could encounter more than one attack type at once. Thus, in this paper, we investigate the resilience of state-of-the-art data-driven CDSs against replay false data injection, adversarial evasion, and adversarial data poisoning attacks on a realistic IEEE 118-bus system model. It turns out that a convolutional recurrent graph autoencoder-based CDS offers an attack detection rate of 96 – 97.5%, which outperforms other machine learning and deep learning-based data-driven CDSs by 16 – 54% since it captures the recurrent and spatial aspects of the data without being trained on attack data.

关键词： Deep learning Europe Feature extraction Graph neural networks Data models Smart grids Reliability Computer crime Unsupervised learning Resilience

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Terahertz Time-Domain Imaging for Radiotherapy Treatment Monitoring in Pancreatic Ductal Adenocarcinoma

Terahertz Time-Domain Imaging for Radiotherapy Treatment Mon...

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CLEO: Applications and Technology in CLEO 2024, CLEO: A and T 2024 - Part of Conference on Lasers and Electro-Optics

作者： Chakraborty, Debamitra Mills, Bradley N. Cheng, Jing Komissarov, Ivan Gerber, Scott Sobolewski, Roman Materials Science Graduate Program University of Rochester RochesterNY14627 United States Laboratory for Laser Energetics University of Rochester RochesterNY14623-1299 United States Department of Surgery University of Rochester Medical Center RochesterNY14642 United States Department of Electrical and Computer Engineering University of Rochester RochesterNY14627-0231 United States

ISBN: (纸本)9781957171395

Terahertz time-domain imaging was performed of stereotactic body radiotherapy-treated murine pancreatic ductal adenocarcinoma (PDAC) with a high spatial resolution. To generate 2D maps of the tissue samples, the refractive index and absorption coefficient were used as imaging markers extracted with the maximum a-posteriori probability (MAP) estimation algorithm. The results were then compared with the terahertz maps of untreated murine PDAC tissue samples and demonstrated that terahertz imaging can probe physical changes with high sensitivity in the tissue post treatment. © Optica Publishing Group 2024, © 2024 The Author(s)

关键词： Tissue

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Applications of Superpixels on Phase Contrast Microscopy 5

Applications of Superpixels on Phase Contrast Microscopy

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5th International Conference on Transdisciplinary AI, TransAI 2023

作者： On, Vincent Zahedi, Atena Phandthong, Rattapol California State University Bakersfield Department of Computer and Electrical Engineering and Computer Science BakersfieldCA United States Stem Cell Research Center University of California Irvine IrvineCA United States University of California Riverside Department of Molecular Cell and Systems Biology RiversideCA United States

ISBN: (纸本)9798350358018

Cellular microscopy is enhanced by computational paradigms such as image processing, computer vision, and machine learning. Image segmentation is vital for quantifying cell images, enabling tracking and subsequent applications. Superpixel algorithms offer a promising solution to challenges in phase contrast image segmentation, as they group pixels with similar characteristics into meaningful regions. By preserving essential structural information and reducing complexity, superpixels improve accuracy in cell-related measurements and facilitate the study of dynamic cellular processes. This paper presents a comparison of popular superpixel algorithms applied to cellular microscopy, highlighting their contributions to image analysis and interpretation in cell biology research. © 2023 IEEE.

关键词： Image segmentation

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Recent progress in solution-processed flexible organic photovoltaics

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npj Flexible Electronics 2022年第1期6卷 871-884页

作者： Lulu Sun Kenjiro Fukuda Takao Someya Thin-Film Device Laboratory RIKEN2-1 HirosawaWakoSaitama 351-0198Japan Center for Emergent Matter Science(CEMS) RIKEN2-1 HirosawaWakoSaitama 351-0198Japan Department of Electrical Engineering and Information Systems School of EngineeringThe University of Tokyo7-3-1 HongoBunkyo-kuTokyo 113-8656Japan

The certified power conversion efficiency(PCE)of organic photovoltaics(OPV)fabricated in laboratories has improved dramatically to over 19%owing to the rapid development of narrow-bandgap small-molecule acceptors and wide bandgap polymer donor *** next pivotal question is how to translate small-area laboratory devices into large-scale commercial *** requires the OPV to be solution-processed and flexible to satisfy the requirements of high-throughput and large-scale production such as roll-to-roll *** review summarizes and analyzes recent progress in solution-processed flexible *** a detailed discussion from the perspective of the behavior of the narrow bandgap small-molecule acceptor and wide bandgap polymer donor active layer in solution-processed flexible devices,the existing challenges and future directions are discussed.

关键词： donor narrow translate

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Influence of red wine marination on the quality,safety,and stability of goat Coalho cheese produced in the Brazilian semi-arid region

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Food Bioscience 2023年第3期53卷 2087-2097页

作者： Mônica Tejo Cavalcanti Leidiana Elias Xavier Bruno Fonsêca Feitosa Winnie Alencar-Luciano Valker Araujo Feitosa Evandro Leite de Souza Mônica Correia Gonçalves Graduation Program in Agroindustrial Systems Center for Agro-Food Science and TechnologyFederal University of Campina GrandePombalParaíbaBrazil Faculty of Food Engineering State University of CampinasCampinasSão PauloBrazil National Institute of the Semi-arid Region Campina GrandeParaíbaBrazil Center for Biological and Health Sciences(CCBS) Federal University of ViçosaViçosaMinas GeraisBrazil Laboratory of Food Microbiology Department of NutritionCenter for Health SciencesFederal University of ParaíbaJoão PessoaParaíbaBrazil

The manufacture of goat Coalho cheese has become a potential mechanism for market expansion and *** study aimed to evaluate the influence of marination in red wine on the quality,safety,and stability of goat Coalho cheese produced in the Brazilian semi-arid *** were immersed in red wine at 6±1℃ for seven *** MC(marinated cheese)and CC(control cheese)were characterized during 28 days of storage(4±1℃).At the initial time(day zero),an irregular,porous,and spongy surface related to the moisture,fat globules,and protein network was observed on CC and MC,which had a similar fatty acid profile(17 saturated fatty acids,9 monounsaturated fatty acids,and 8 polyunsaturated fatty acids).The marination resulted in lower pH,greater hardness and chewiness,and less Na in the matrix(CC-253.12 mg.100 g^(-1);MC-129.02 mg.100 g^(-1)).Over 28 days,marination delayed MC degradation rates(lipid oxidation and proteolysis),with greater pro-tection against free radicals,a higher amount of total phenolic compounds,and antioxidant *** re-sults indicated a positive effect of wine marination on the physicochemical parameters of goat Coalho cheese,improving the quality,safety,and stability.

关键词： Goat cheese Marination Lipid oxidation Proteolysis Phenolics Cheese microstructure

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Text-Guided Multi-Modal Fusion for Underwater Visual Tracking

Text-Guided Multi-Modal Fusion for Underwater Visual Trackin...

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IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： Yonathan Michael Mohamad Alansari Sajid Javed Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates Center for Autonomous Robotic Systems Khalifa University Abu Dhabi United Arab Emirates

ISBN: (数字)9798350374285

ISBN: (纸本)9798350374292

The integration of Natural Language (NL) descriptions with contemporary tracking algorithms constitutes a new and dynamic field, exhibiting no indications of deceleration in the near future. Nevertheless, the absence of comprehensive language descriptions for tracking datasets, particularly in the domain of underwater tracking datasets, presents a substantial impediment to the advancement of this field. Typically, the textual descriptions accompanying these datasets are brief, inadequately informative, lack details regarding relative location or directional movement, and occasionally deviate from the manner in which a human would naturally describe the target in ordinary conversation. In response to this challenge, we propose the development of vividly descriptive NL descriptions tailored for the UVOT400 dataset, which focuses on underwater tracking. These descriptions aim to encapsulate a myriad of factors in order to furnish as comprehensive an understanding as possible regarding the target fish. Subsequent evaluations of these descriptions, conducted in conjunction with contemporary language-based tracking systems, have revealed superior performance in comparison to the best-performing visual-only trackers employed for benchmarking purposes with the aforementioned dataset.

关键词： Visualization Target tracking Heuristic algorithms Surveillance Refining Natural languages Oral communication

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