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Effect of pulsed current cathodic protection on pipeline steel API 5L X65 corrosion mitigation: An investigation and machine learning-assisted modeling

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Corrosion Communications 2023年第4期12卷 29-45页

作者： Hosein Eslamian Mehdi Javidi Mohammad Reza Zamani Mohammad Mahdi Dana Eghbal Mansoori Department of Materials Science and Engineering School of Engineering Shiraz University School of Metallurgy and Materials Engineering College of Engineering University of Tehran Computer Science Eng.and IT Dept. School of Electrical and Computer Engineering Shiraz University

Pulsed current cathodic protection(PCCP) could be more effective than direct current cathodic protection(DCCP)for mitigating corrosion in buried structures in the oil and gas industries if appropriate pulsed parameters are chosen. The purpose of this research is to present the corrosion prevention mechanism of the PCCP technique by taking into account the effects of duty cycle as well as frequency, modeling the relationships between pulse parameters(frequency and duty cycle) and system outputs(corrosion rate, protective current and pipe-to-soil potential) and finally identifying the most effective protection conditions over a wide range of frequency(2–10 kHz) and duty cycle(25%-75%). For this, pipe-to-soil potential, pH, current and power consumption, corrosion rate, surface deposits and investigation of pitting corrosion were taken into account. To model the input-output relationship in the PCCP method, a data-driven machine learning approach was used by training an artificial neural network(ANN). The results revealed that the PCCP system could yield the best protection conditions at 10 kHz frequency and 50% duty cycle, resulting in the longest protection length with the lowest corrosion rate at a consumption current 0.3 time that of the DCCP method. In the frequency range of 6–10 kHz and duty cycles of 50%-75%, SEM images indicated a uniform distribution of calcite deposits and no pits on cathode surface.

关键词： Pulsed current cathodic protection Duty cycle Frequency Artificial neural network API X65 pipeline steel

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Non-planar dielectrics derived thermal and electrostatic field inhomogeneity for boosted weather-adaptive energy harvesting

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National science Review 2023年第9期10卷 248-257页

作者： Yi Zhou Tianpeng Ding Yin Cheng Yi Huang Wu Wang Jianmin Yang Lin Xie Ghim Wei Ho Jiaqing He Shenzhen Key Laboratory of Thermoelectric Materials and Department of Physics Southern University of Science and Technology Department of Electrical and Computer Engineering National University of Singapore School of Electronic Science and Engineering State Key Laboratory of Electronic Thin Film and Integrated Devices University of Electronic Science and Technology of China Department of Materials Science and Engineering National University of Singapore Institute of Materials Research and Engineering Agency for ScienceTechnology and Research (A*STAR) GuangdongHong Kong-Macao Joint Laboratory for Photonic-Thermal-Electrical Energy Materials and Devices Southern University of Science and Technology

Weather-adaptive energy harvesting of omnipresent waste heat and rain droplets,though promising in the field of environmental energy sustainability,is still far from practice due to its low electrical output owing to diele ctric structure irrationality and *** we present atypical upcycling of ambient heat and raindrop energy via an all-in-one non-planar energy harvester,simultaneously increasing solar pyroelectricity and droplet-based triboelectricity by two-fold,in contrast to conventional *** delivered non-planar dielectric with high transmittance confines the solar irradiance onto a focal hotspot,offering transverse thermal field propagation towards boosted inhomogeneous polarization with a generated power density of 6.1 mW m-2at 0.2 ***,the enlarged lateral surface area of curved architecture promotes droplet spreading/separation,thus travelling the electrostatic field towards increased *** enhanced pyroelectric and triboelectric outputs,upgraded with advanced manufacturing,demonstrate applicability in adaptive sustainable energy harvesting on sunny,cloudy,night,and rainy *** findings highlight a facile yet efficient strategy,not only for weather-adaptive environmental energy recovery but also in providing key insights for spatial thermal/electrostatic field manipulation in thermo electrics and ferroelectrics.

关键词： non-planar dielectrics heat manipulation pyroelectrics weather-adaptive energy harvesting

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Robust Graph Autoencoder-Based Detection of False Data Injection Attacks Against Data Poisoning in Smart Grids

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1287-1301页

作者： Takiddin, Abdulrahman Ismail, Muhammad Atat, Rachad Davis, Katherine R. Serpedin, Erchin Department of Electrical and Computer Engineering Texas A and M University College StationTX77843 United States Department of Computer Science Tennessee Tech University CookevilleTN38505 United States Department of Electrical and Computer Engineering Program Texas A and M University Qatar Doha23874 Qatar

Machine learning-based detection of false data injection attacks (FDIAs) in smart grids relies on labeled measurement data for training and testing. The majority of existing detectors are developed assuming that the adopted datasets for training have correct labeling information. However, such an assumption is not always valid as training data might include measurement samples that are incorrectly labeled as benign, namely, adversarial data poisoning samples, which have not been detected before. Neglecting such an aspect makes detectors susceptible to data poisoning. Our investigations revealed that detection rates (DRs) of existing detectors significantly deteriorate by up to 9-29% when subject to data poisoning in generalized and topology-specific settings. Thus, we propose a generalized graph neural network-based anomaly detector that is robust against FDIAs and data poisoning. It requires only benign datasets for training and employs an autoencoder with Chebyshev graph convolutional recurrent layers with attention mechanism to capture the spatial and temporal correlations within measurement data. The proposed convolutional recurrent graph autoencoder model is trained and tested on various topologies (from 14, 39, and 118-bus systems). Due to such factors, it yields stable generalized detection performance that is degraded by only 1.6-3.7% in DR against high levels of data poisoning and unseen FDIAs in unobserved topologies. Impact Statement-Artificial Intelligence (AI) systems are used in smart grids to detect cyberattacks. They can automatically detect malicious actions carried out bymalicious entities that falsifymeasurement data within power grids. Themajority of such systems are data-driven and rely on labeled data for model training and testing. However, datasets are not always correctly labeled since malicious entities might be carrying out cyberattacks without being detected, which leads to training on mislabeled datasets. Such actions might degrade the d

关键词： Cybersecurity

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Simultaneous improving luminescence intensity and stability of CsPbBr_(3):SCN−@Eu/Zr-Uio-66-NH_(2) with tunable emissions from blue to green and applications in indoor photovoltaics

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Nano Research 2024年第8期17卷 6879-6887页

作者： Minze Li Yao Sun Honglai Lu Peifen Zhu Ruihong Wang Guofeng Wang Key Laboratory of Functional Inorganic Material Chemistry Ministry of EducationSchool of Chemistry and Materials ScienceHeilongjiang UniversityHarbin 150080China Department of Electrical Engineering and Computer Science University of MissouriColumbiaMO 65211USA

The construction of stable and efficient materials that emit blue and green light remains a *** the blue light materials reported,metal-organic framework(MOF)materials are rarely reported as blue phosphors due to their weak luminescence *** on the construction of CsPbBr_(3)@MOF(CPB@MOF),an innovative idea was proposed to simultaneously enhance the green luminescence of CPB and the blue luminescence of MOF through the interaction between CPB and MOF for the first *** expected,the blue luminescence from CPB:7%SCN−@0.5%MOF:Eu as well as the green luminescence from 5%CPB:7%SCN−@MOF:Eu was sufficient to construct high-performance light-emitting diode(LED)devices and further excite solar cells to generate stable photoelectric *** white LED(WLED)device with excellent color quality(color rendering index(CRI)=96.2)and correlated color temperature(CCT=9688 K)can be constructed by using the obtained blue-emitting CPB:7%SCN-@0.5%MOF:Eu,green-emitting 5%CPB:7%SCN−@MOF:Eu,and red-emitting PPB:30%Mn^(2+).The density functional theory(DFT)theoretical calculation results indicate that the p orbital of Pb plays the major role in the conduction band,and the p orbital of Br plays the major role in the valance band of CPB and CPB:SCN−.While the p orbital of O plays the major role in both the conduction band and valance band of *** heat capacity of CPB and CPB:SCN−separately reaches the Dulong–Petit limit at 200 and 400 K,indicating that the thermal stability of CsPbBr_(3)increases after SCN−doping.

关键词： CsPbBr_(3):SCN−@Uio-66-NH_(2) composites luminescence stability white light-emitting diodes(WLEDs) indoor photovoltaics density functional theory(DFT)calculation

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Boost DC-DC converter with regenerative snubber

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Energy Reports 2024年 12卷 798-819页

作者： Yau, Yeu-Torng Department of Ph.D. Program Prospective Technology of Electrical Engineering and Computer Science National Chin-Yi University of Technology Taichung Taiwan Department of Electrical Engineering National Chin-Yi University of Technology Taichung Taiwan

To accommodate the wide range of input voltages supplied by redundant batteries and ensure an adequate hold-up time for communication systems during utility power failures, power supplies used in 5 G base stations typically include a front-end boost converter as the first stage and a second-stage isolated converter. In this paper, a new topology for a regenerative snubber in boost converters is proposed. Compared to traditional active or passive snubber topologies, the proposed snubber offers the advantages of having fewer components and occupying less space. Moreover, it does not require additional active devices and can be driven by widely-used commercial PWM control integrated circuits, achieving a higher level of cost-effectiveness and offering increased industrial applications. The final prototype of a power supply for a networking switch hub (as used in 5 G base stations) is fabricated to demonstrate the feasibility of the proposed method. © 2024 The Authors

关键词： Topology

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Giant phonon-skyrmion coupling in ferromagnet/heavy metal heterostructures

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Physical Review B 2024年第6期110卷 064423-064423页

作者： Egor Savostin Vitaliy Lomakin Program in Materials Science and Engineering Center for Memory and Recording Research Department of Electrical and Computer Engineering

The strong impact of the strain-induced Dzyaloshinskii-Moriya interaction (SIDMI) on the magnetization dynamics of skyrmions in nanomagnetic structures is demonstrated. The effects of SIDMI are characterized by skyrmion equations (SEs) of motion and magnetoelastic (ME) equations. The study is performed on a model system of MgO/CoFe/Pt stacked on a piezoelectric substrate. The results demonstrate a major nonlinear amplification in both the first- and higher-harmonic magnitudes of the skyrmion breathing mode due to SIDMI. Remarkably, this enhancement can trigger a skyrmion collapse, enabling its deletion with ultraweak strain-induced excitations. The SIDMI effect is shown to be much more significant than the conventional ME effect. These findings open different avenues for the efficient manipulation of nanomagnetic structures through strain.

关键词： Exchange interaction Magnetic interactions Magnetization dynamics Magnetoacoustic effect Micromagnetism Spin dynamics

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Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection

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Neural Computing and Applications 2025年第6期37卷 4567-4583页

作者： Sayem, Faizul Rakib Islam, Md. Sakib Bin Naznine, Mansura Nashbat, Mohammad Hasan-Zia, Mazhar Kunju, Ali K Ansaruddin Khandakar, Amith Ashraf, Azad Majid, Molla Ehsanul Kashem, Saad Bin Abul Chowdhury, Muhammad E. H. Department of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh Department of Biomedical Engineering Military Institute of Science and Technology Dhaka Bangladesh Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Chemical Engineering Department University of Doha for Science and Technology Doha Qatar Department of Electrical Engineering Qatar University Doha2713 Qatar Computer Applications Department Academic Bridge Program Qatar Foundation Doha Qatar Department of Computing Science AFG College With the University of Aberdeen Doha Qatar

Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material composition and volume of urban solid waste are key considerations in processing, managing, and utilizing city waste. Deep learning technologies have emerged as viable solutions to address waste management issues by reducing labor costs and automating complex tasks. However, the limited number of trash image categories and the inadequacy of existing datasets have constrained the proper evaluation of machine learning model performance across a large number of waste classes. In this paper, we present robust waste image classification and object detection studies using deep learning models, utilizing 28 distinct recyclable categories of waste images comprising a total of 10,406 images. For the waste classification task, we proposed a novel dual-stream network that outperformed several state-of-the-art models, achieving an overall classification accuracy of 83.11%. Additionally, we introduced the GELAN-E (generalized efficient layer aggregation network) model for waste object detection tasks, obtaining a mean average precision (mAP50) of 63%, surpassing other state-of-the-art detection models. These advancements demonstrate significant progress in the field of intelligent waste management, paving the way for more efficient and effective solutions. © The Author(s) 2024.

关键词： Deep learning for waste management Intelligent waste management Object detection in waste management Sustainable waste processing Urban solid waste Waste image classification

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A ShuffleNet Architecture-Based Multi-Layer Feature Scaling and Fusion Approach for Underwater Net Cage Damage Detection in Aquaculture

A ShuffleNet Architecture-Based Multi-Layer Feature Scaling ...

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2024 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2024

作者： Huang, Xiang-Rui Chen, Yi-Cheng Chen, Liang-Bi Hu, Wu-Chih National Penghu University of Science and Technology Master Program in Electrical Engineering and Computer Science Department of Electrical Engineering Penghu Taiwan National Penghu University of Science and Technology Department of Computer Science and Information Engineering Penghu Taiwan

ISBN: (纸本)9798331530839

In offshore aquaculture operations, personnel equipped with diving gear are often necessary to inspect the underwater net cages for damage, particularly on the sea floor. This manual inspection process is time-consuming and complex. To overcome this problem, this paper proposes a computer vision solution for identifying damage in underwater net cages to address the inefficiencies and challenges of traditional manual inspections. The proposed scheme utilizes a high-performance multi-branch computational architecture designed based on ShuffleNet architecture to detect net cage damage more efficiently. Experimental results demonstrate that this work performs well on the ImageNet ILSVRC-2010 dataset and achieves an accuracy of 88.54% in underwater net damage detection. © 2024 IEEE.

关键词： Aquaculture

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Phase-Field Model of Electronic Antidoping

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Physical Review Letters 2024年第25期132卷 256502-256502页

作者： Yin Shi Guo-Dong Zhao Ismaila Dabo Shriram Ramanathan Long-Qing Chen Department of Materials Science and Engineering Department of Electrical and Computer Engineering

Charge carrier doping usually reduces the resistance of a semiconductor or insulator, but was recently found to dramatically enhance the resistance in certain series of materials. This remarkable antidoping effect has been leveraged to realize synaptic memory trees in nanoscale hydrogenated perovskite nickelates, opening a new direction for neuromorphic computing. To understand these phenomena, we formulate a physical phase-field model of the antidoping effect based on its microscopic mechanism and simulate the voltage-driven resistance change in the prototypical system of hydrogenated perovskite nickelates. Remarkably, the simulations using this model, containing only one adjustable parameter whose magnitude is justified by first-principles calculations, quantitatively reproduce the experimentally observed treelike resistance states, which are shown unambiguously to arise from proton redistribution-induced local band gap enhancement and carrier blockage. Our work lays the foundation for modeling the antidoping phenomenon in strongly correlated materials at the mesoscale, which can provide guidance to the design of novel antidoping-physics-based devices.

关键词： Doping effects Neural basis of learning & memory Strongly correlated systems Phase-field modeling

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Emergent classical spin liquid phases in an Ising lattice via size effects

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Physical Review B 2024年第2期109卷 L020303-L020303页

作者： Pratik Brahma Sayeef Salahuddin Department of Electrical Engineering and Computer Sciences University of California Berkeley California 94720 USA Materials Science Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA

We show that a classical spin liquid phase can emerge from an ordered magnetic state in the two-dimensional frustrated Shastry-Sutherland Ising lattice due to lateral confinement. Two distinct classical spin liquid states are stabilized: (i) long-range spin-correlated dimers, and (ii) exponentially decaying spin-correlated disordered states, depending on widths of W=3n, 3n+1 or W=3n+2,n being a positive integer. Stabilization of spin liquids in a square-triangular lattice moves beyond the conventional geometric paradigm of kagome, triangular, or tetrahedral arrangements of antiferromagnetic ions, where spin liquids have been discussed conventionally.

关键词： Phase diagrams Spin liquid Classical spin models Lattice types Ising model Transfer matrix calculations

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