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Electronic band energy of a bent ZnO piezoelectric semiconductor nanowire

Applied Mathematics and Mechanics(English Edition) 2020年第6期41卷 833-844页

作者： Wanli YANG Yuantai HU E.N.PAN Department of Mechanics Hubei Key Laboratory of Engineering Structural Analysis and Safety AssessmentHuazhong University of Science and TechnologyWuhan 430074China Computer Modeling and Simulation Group College of EngineeringUniversity of AkronAkronOhio 44325U.S.A.

The electric band energy variation in a bent piezoelectric semiconductor(PSC) nanowire of circular cross-section induced by the mechanical force is analyzed based on a six-band k · p method. The electric-mechanical fields are first obtained analytically in a cantilever bent PSC nanowire by solving the fully-coupled electro-mechanical equations. Then, the band energy is acquired numerically via the six-band *** considering further the nonlinear coupling between the piezoelectric and semiconducting quantities, the contribution of the redistribution carriers to the electric field is analyzed from the Gauss’ s law. Numerical examples are carried out for an n-type Zn O nanowire in different locations induced by an applied concentrated end force. They include the electric potential, heavy hole(HH), light hole(LH), spin-orbit split-off(SO),and conduction band(CB) edges along the axial and thickness directions. Our results show that the applied force has a significant effect on the band energies. For instance, on the bottom surface along the axial direction, the bandgaps near the fixed end are greater than those near the loading end, and this trend is reversed on the top surface. Moreover,at a fixed axial location, the energy level of the lower side can be enhanced by applying a bending force at the end. The present results could be of significant guidance to the electronic devices and piezotronics.

关键词： piezoelectric semiconductor(PSC) mechanical bending energy band ZnO

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Neuro-symbolic Explainable Artificial Intelligence Twin for Zero-touch IoE in Wireless Network

arXiv

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arXiv 2022年

作者： Munir, Shirajum Kim, Ki Tae Adhikary, Apurba Saad, Walid Shetty, Sachin Park, Seong-Bae Hong, Choong Seon Virginia Modeling Analysis and Simulation Center Department of Computational Modeling and Simulation Engineering Old Dominion University SuffolkVA23435 United States Department of Computer Science and Engineering Kyung Hee University Yongin-si17104 Korea Republic of Department of Computer Science and Engineering School of Computing Kyung Hee University Kyung Hee University Yongin-si17104 Korea Republic of Wireless@VT Group Bradley Department of Electrical and Computer Engineering Virginia Tech ArlingtonVA22203 United States

Explainable artificial intelligence (XAI) twin systems will be a fundamental enabler of zero-touch network and service management (ZSM) for sixth-generation (6G) wireless networks. A reliable XAI twin system for ZSM requires two composites: an extreme analytical ability for discretizing the physical behavior of the Internet of Everything (IoE) and rigorous methods for characterizing the reasoning of such behavior. In this paper, a novel neuro-symbolic explainable artificial intelligence twin framework is proposed to enable trustworthy ZSM for a wireless IoE. The physical space of the XAI twin executes a neural-network driven multivariate regression to capture the time-dependent wireless IoE environment while determining unconscious decisions of IoE service aggregation. Subsequently, the virtual space of the XAI twin constructs a directed acyclic graph (DAG)-based Bayesian network that can infer a symbolic reasoning score over unconscious decisions through a first-order probabilistic language model. Furthermore, a Bayesian multi-arm bandits-based learning problem is proposed for reducing the gap between the expected explained score and current obtained score of the proposed neuro-symbolic XAI twin. To address the challenges of extensible, modular, and stateless management functions in ZSM, the proposed neuro-symbolic XAI twin framework consists of two learning systems: 1) implicit learner that acts as an unconscious learner in physical space, and 2) explicit leaner that can exploit symbolic reasoning based on implicit learner decisions and prior evidence. Experimental results show that the proposed neuro-symbolic XAI twin can achieve around 96.26% accuracy while guaranteeing from 18% to 44% more trust score in terms of reasoning and closed-loop automation. © 2022, CC BY-NC-ND.

关键词： Internet of Everything

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Multi-Stage Expansion Planning for Decarbonizing Thermal Generation Supported Renewable Power Systems Using Hydrogen and Ammonia Storage

arXiv

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arXiv 2023年

作者： Yu, Zhipeng Lin, Jin Liu, Feng Li, Jiarong Chi, Yingtian Song, Yonghua Ren, Zhengwei State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing100087 China Sichuan Energy Internet Research Institute Tsinghua University Chengdu610213 China Department of Electrical and Computer Engineering University of Macau 999078 China Department of Electrical Engineering Tsinghua University Beijing100087 China

—Large-scale centralized development of wind and solar energy and peer-to-grid transmission of renewable energy source (RES) via high voltage direct current (HVDC) has been regarded as one of the most promising ways to achieve goals of peak carbon and carbon neutrality in China. Traditionally, large-scale thermal generation is needed to economically support the load demand of HVDC with a given profile, which in turn raises concerns about carbon emissions. To address the issues above, hydrogen energy storage system (HESS) and ammonia energy storage system (AESS) are introduced to gradually replace thermal generation, which is represented as a multi-stage expansion planning (MSEP) problem. Specifically, first, HESS and AESS are established in the MSEP model with carbon emission reduction constraints, and yearly data with hourly time resolution are utilized for each stage to well describe the intermittence of RES. Then, a combined Dantzig-Wolfe decomposition (DWD) and column generation (CG) solution approach is proposed to efficiently solve the large-scale MSEP model. Finally, a real-life system in China is studied. The results indicate that HESS and AESS have the potential to handle the intermittence of RES, as well as the monthly imbalance between RES and load demand. Especially under the goal of carbon neutrality, the contribution of HESS and AESS in reducing levelized cost of energy (LCOE) reaches 12.28% and 14.59%, respectively, which finally leads to a LCOE of 0.4324 RMB/kWh. Copyright © 2023, The Authors. All rights reserved.

关键词： Carbon

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Optimal Sizing of Isolated Renewable Power Systems with Ammonia Synthesis: Model and Solution Approach

arXiv

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arXiv 2023年

作者： Yu, Zhipeng Lin, Jin Liu, Feng Li, Jiarong Zhao, Yuxuan Song, Yonghua The State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing100087 China Sichuan Energy Internet Reasearch Institute Tsinghua University Chengdu610213 China The Department of Electrical and Computer Engineering University of Macau 999078 China The Department of Electrical Engineering Tsinghua University Beijing100087 China

Isolated renewable power to ammonia (IRePtA) has been recognized as a promising way to decarbonize the chemical industry. Optimal sizing of the renewable power system is significant to improve the techno-economic of IRePtA since the investment of power sources exceeds 80% of the total investment. However, multi-timescale electricity, hydrogen, and ammonia storages, minimum power supply for system safety, and the multiyear uncertainty of renewable generation lead to difficulties in planning. To address the issues above, an IGDT-MILFP model is proposed. First, the levelized cost of ammonia (LCOA) is directly formulated as the objective, rendering a mixed integer linear fractional programming (MILFP) problem. Information gap decision theory (IGDT) is utilized to handle the multiyear uncertainty of renewable generation. Second, a combined Charnes-Cooper (C&C) transformation and Branch-and-Bound (B&B) method is proposed to efficiently solve the large-scale IGDT-MILFP model, giving robust and opportunistic planning results. Then, Markov Chain Monte Carlo (MCMC) sampling-based posteriori analysis is leveraged to quantify the long-run performance. Finally, a real-life system in Inner Mongolia, China, is studied. The results indicate that the proposed methods could reduce the computational burden by orders of magnitude for solving a large-scale MILFP problem. Moreover, the proposed IGDT-MILFP model is necessary and accurate to obtain an optimal capacity allocation with the lowest expected LCOA (3610 RMB/t) in long-run simulations. Copyright © 2023, The Authors. All rights reserved.

关键词： Ammonia

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Texture Analysis of Breast US Images Using Morphological Transforms, Hausdorff Dimension and Bagging Ensemble Method

Texture Analysis of Breast US Images Using Morphological Tra...

引用

Mediterranean Conference on Control and Automation (MED)

作者： Gigi Tăbăcaru Simona Moldovanu Marian Barbu Department of Automatic Control and Electrical Engineering Faculty of Automation Computers Electrical Engineering and Electronics “Dunarea de Jos” University of Galati Romania Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics “Dunarea de Jos” University of Galati The Modelling & Simulation Laboratory Dunarea de Jos University of Galati Galati Romania

ISBN: (数字)9798350395440

ISBN: (纸本)9798350395457

In this work, an attempt is made for the first time to use the measurement pattern generated by morphological transformation quantified by Hausdorff fractal dimension (HFD) and classified with ensemble learning based on bagging. The proposed work uses three morphological transformations for image preprocessing: hit-and-miss transform (HMT), white (WHT), and black top-hat (BHT). The pattern texture of US breast images is described by extracting the HFD from the regions of interest (ROI) after the ultrasound (US) images have been preprocessed. The main objective of this study was achieved by comparatively analyzing the classification performance of features using the Random Forest (RF), Extra Trees (ET) classifier, and bagging ensemble method based on XGBoot classifier. In presented study, the XGBoost classifier and BHT image processing method give an accuracy of 89.8% in a binary classification, benign versus malignant breast cancer.

关键词： Ultrasonic imaging Transforms Feature extraction Time measurement Fractals Ensemble learning Image preprocessing

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Virtual reality simulation in training endoscopic skills: A systematic review

引用

Laparoscopic, Endoscopic and Robotic Surgery 2021年第4期4卷 97-104页

作者： Wenlan Zhang Xin Liu Bin Zheng Surgical Simulation Research Laboratory Department of SurgeryUniversity of AlbertaEdmontonT6G 2E1Canada School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China

Objective:Training young physicians in gastrointestinal endoscopy through virtual reality(VR)simulators has become *** is important to evaluate the benefits and usefulness of this technology for teaching endoscopic *** objective of this literature review is to understand the benefits of VR technology through quantitative and qualitative examination of learning ***:A literature search of 7 databases was *** which compared the effects of learning through VR simulation and another method of learning were *** addition,studies were included if they evaluated learning outcomes on clinical *** could be medical residents,fellows,physicians,or *** outcomes measured across studies included subjective overall performance scores,total procedure times,rate of successful procedure completion,error rates,patient pain or discomfort,and measures of ***:A total of 22 studies were ***,VR simulation training was seen to be comparable or significantly better than clinical training,no training,other types of simulation,and another form of VR *** authors reported increasing patient safety,reducing stress and time constraints,and shortening the learning curve as advantages of VR simulation ***,this form of training is also expensive and may result in the learning of bad ***:VR simulation technology can be a valuable form of educating endoscopy novices if properly supervised during training,and if there is also integration of clinical training.

关键词： Gastrointestinal endoscopy Virtual reality simulation Physician Training Endoscopic skill

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FISS GAN:A Generative Adversarial Network for Foggy Image Semantic Segmentation

引用

IEEE/CAA Journal of Automatica Sinica 2021年第8期8卷 1428-1439页

作者： Kunhua Liu Zihao Ye Hongyan Guo Dongpu Cao Long Chen Fei-Yue Wang School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou 510006China State Key Laboratory of Automotive Simulation and Control and the Department of Control Science and Engineering Jilin University(Campus Nanling)Changchun 130025China Mechanical and Mechatronics Engineering Department at the University of Waterloo 200 University Avenue West WaterlooON N2L 3G1Canada State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China

Because pixel values of foggy images are irregularly higher than those of images captured in normal weather(clear images),it is difficult to extract and express their *** method has previously been developed to directly explore the relationship between foggy images and semantic segmentation *** investigated this relationship and propose a generative adversarial network(GAN)for foggy image semantic segmentation(FISS GAN),which contains two parts:an edge GAN and a semantic segmentation *** edge GAN is designed to generate edge information from foggy images to provide auxiliary information to the semantic segmentation *** semantic segmentation GAN is designed to extract and express the texture of foggy images and generate semantic segmentation *** on foggy cityscapes datasets and foggy driving datasets indicated that FISS GAN achieved state-of-the-art performance.

关键词： Edge GAN foggy images foggy image semantic segmentation GAN semantic segmentation

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Biomechanical models in the lower-limb exoskeletons development: A review

TechRxiv

引用

TechRxiv 2023年

作者： Firouzi, Vahid Seyfarth, Andre Song, Seungmoon Von Stryk, Oskar Sharbafi, Maziar Ahmad Lauflabor Locomotion Laboratory Institute of Sport Science TU Darmstadt Germany Simulation Systems Optimization and Robotics Group Department of Computer Science TU Darmstadt Germany Mechanical and Industrial Engineering Northeastern University United States

Lower limb exoskeletons serve multiple purposes, like supporting and augmenting movement. Biomechanical models are practical tools to understand human movement, and motor control. This review explores the applications of biomechanical gait models in the development of lower-limb exoskeletons. Biomechanical models can be broadly categorized as conceptual and detailed models. Biomechanical models can be used for the design, control, and assessment of exoskeletons. Also, these models can be used to estimate unmeasurable or hard-to-measure variables, which is also useful within the aforementioned applications. The analysis of applications and research areas aims to inspire further advancements in this field and contribute to the development of more effective exoskeletons. We highlight validating simulation studies, improving the accuracy and fidelity of biomechanical models, and benefiting from exoskeletons to enhance or validate biomechanical models as future research areas related to biomechanical models. We also emphasize the exploration of model-based design and control approaches for exoskeletons targeting pathological gait, and utilizing biomechanical models for diverse design objectives of exoskeletons. Also, increasing the availability of open-source resources accelerates exoskeleton and biomechanical model advancement. Our review reveals existing needs and cracks new perspectives for the development of more usable and human-compatible exoskeletons based on biomechanical models. © 2023, CC BY.

关键词： Exoskeleton (Robotics)

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Detection of Detonators in Baggage Using Deep CNN Architecture: A Comparative Study of Contrast Enhancement and Feature Extraction Methods

Detection of Detonators in Baggage Using Deep CNN Architectu...

引用

International Conference on Image and Signal Processing and their Applications (ISPA)

作者： Oulhissane Lynda Merah Mostefa Simona Moldovanu Luminita Moraru Laboratory of Signals and Systems LSS Faculty of Science and Technology Abdelhamid Ibn Badis University Mostaganem Algeria MSlab Department of Computer Science and Information Technology Modelling & Simulation Laboratory Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Galati Romania MSlab Department of Chemistry Physics & Environment Modelling & Simulation Laboratory Faculty of Sciences and Environment Dunarea de Jos University of Galati Galati Romania

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Visual inspection of dual-energy X-ray radiographic images of cabin baggage requires high performance, but is hindered by various challenges such as low target prevalence, variability in target visibility, possible presence of multiple targets, and security personnel fatigue and inattention. Artificial intelligence (AI) techniques, particularly deep convolutional neural networks (CNNs), have shown promise in improving the automatic detection of explosives, even with low-resolution radiographic images, especially in high baggage throughput scenarios. In this paper, we focus on the detection of detonators as components of improvised explosive devices. The proposed approach involves comparing two experiments implemented in a deep CNN architecture using TensorFlow and Keras libraries. In the first experiment, raw dual-energy radiographic images without any enhancement were used. The second experiment includes three methods for contrast enhancement and feature extraction: the Contrast Limited Adaptive Histogram Equalization (CLAHE) method, the wavelet transform-based method, and the mixed CLAHE RGB-Wavelet method. In the latter two methods, Haar, Db2, Coif2, and Sym2 mother wavelet functions at two levels (HH and HL) were employed. The analysis of results focuses on a comparative study of performance measures such as accuracy, precision, recall, and F1 score. It was found that the preprocessing methods used in experiment 2, for the two evaluated classes (detonator and no detonator), achieved higher accuracy compared to the raw radiographic images used in experiment 1 (98.08%). The highest accuracies in experiment 2, with a value of 100%, were obtained with the CLAHE method (green channel in grayscale, blue channel in grayscale, and RGB channels) and the wavelet transform method with Haar mother wavelet at two levels HL

关键词： Wavelet transforms Radiography Visualization Gray-scale Inspection Feature extraction Libraries

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Melanoma Detection using a Random Forest Algorithm 10

Melanoma Detection using a Random Forest Algorithm

引用

10th E-Health and Bioengineering Conference, EHB 2022

作者： Damian, Felicia-Anisoara Moldovanu, Simona Moraru, Luminita The Modelling & Simulation Laboratory University Dunarea de Jos of Galati UGAL Galati Romania Department of Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics University Dunarea de Jos of Galati UGAL Galati Romania Department of Chemistry Physics and Environment Faculty of Sciences and Environment University Dunarea de Jos of Galati UGAL Galati Romania

ISBN: (纸本)9781665485579

In recent years, statistical evidence has identified melanoma as a major cause of skin cancer deaths. In this study, we propose a skin analysis lesions based on colour features (C) and Hu (H) moments to differentiate benign (nevi) and malignant (melanoma) lesions and, a classification based on Random Forest algorithm. Colour features are found as follows. Pre-processing for hair removing using the Dull-razor method;image segmentation based on the optimal colour threshold set on each RGB colour channel (red, green, blue);23 colour features extraction using the minimum and maximum RGB intensity and Hu moments;Random Forest (RF) algorithm to accurately differentiate benign and malignant skin lesions. This study used three datasets: 7-Point, Med-Node, PH2. RF classifier performance in terms of accuracy was: 94% for the 7-point dataset with H5, H7, H6, H2, C08 selected as relevant features;98.1% for the Med-Node dataset and H7, H5, H6, H2, H1 ACC the relevant features;and, 96.5% for the PH2 dataset and H5, H6, H7, C03, H1 selected as relevant features, respectively. Shape features based on Hu moments are found to improve the classification performance of the skin lesions compared to the colour features. © 2022 IEEE.

关键词： Image segmentation

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