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arXiv 2021年

作者： Wang, Shengbo Wen, Shiping Shi, Kaibo Zhu, Song Huang, Tingwen School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Australian AI Institute Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia School of Information Science and Engineering Chengdu University Chengdu611040 China School of Mathematics China University of Mining and Technology Xuzhou221116 China Science Program Texas A & M University at Qatar Doha23874 Qatar

The optimal control input for linear systems can be solved from algebraic Riccati equation (ARE), from which it remains questionable to get the form of the exact solution. In engineering, the acceptable numerical solutions of ARE can be found by iteration or optimization. Recently, the gradient descent based numerical solutions has been proven effective to approximate the optimal ones. This paper introduces this method to tracking problem for heterogeneous linear systems. Differently, the parameters in the dynamics of the linear systems are all assumed to be unknown, which is intractable since the gradient as well as the allowable initialization needs the prior knowledge of system dynamics. To solve this problem, the method named dynamic regressor extension and mix (DREM) is improved to estimate the parameter matrices in finite time. Besides, a discounted factor is introduced to ensure the existence of optimal solutions for heterogeneous systems. Two simulation experiments are given to illustrate the effectiveness. © 2021, CC BY-NC-ND.

关键词： Gradient methods

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3D Liver and Tumor Segmentation in CT Scans with Robust Self-Paced Learning

3D Liver and Tumor Segmentation in CT Scans with Robust Self...

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International Conference on Information Science and Control engineering (ICISCE)

作者： Obed Tettey Nartey Guowu Yang Sarpong Kwadwo Asare Jinzhao Wu Dorothy Araba Yakoba Agyapong Lady Nadia Frempong Big Data Research Center School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Mathematics and Physics Guangxi University for Nationalities Nanning China Biomedical Engineering Program Kwame Nkrumah University of Science and Technology Kumasi Ghana

Liver segmentation is a challenging problem where fully supervised deep learning models require large amounts of voxel-wise labels, which are usually laborious, expensive, and time consuming to obtain. However, massive non-annotated 3D CT volumes are usually easily accessible. Most deep learning based works to address semi-supervised CT medical image segmentation problems have mostly been graph-based or adversarial-based. In sharp contrast, this work investigates the potential of a semi-supervised learning method; novel self-training framework utilizing self-paced learning strategy to generate and select pseudo-labels from easy-to-transfer(well represented classes) to hard-to-transfer(less represented classes) samples. The proposed scheme introduces an effective pseudo-label generation and selection algorithm to generate highly confident pseudo-labels, jointly learn and optimize a model which can be solved by alternatively generating pseudo labels on unlabeled target data and re-training the model with the generated labels. Further, we also propose a novel class balancing framework to avoid the gradual eminence of easy-to-transfer discriminative features on pseudo-label generation utilizing class-wise normalization of confidence scores. Comprehensive experiments conducted on publicly available datasets show that the proposed method outperformed other state-of-the-art semi-supervised and some fully supervised methods on the segmentation results of tumors and achieved very competitive performance for liver segmentation.

关键词： Training Deep learning Image segmentation Information science Three-dimensional displays Computed tomography Liver

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Robust Adaptive Safety-Critical Control for Unknown Systems with Finite-Time Element-Wise Parameter Estimation

arXiv

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arXiv 2021年

作者： Wang, Shengbo Lyu, Bo Wen, Shiping Shi, Kaibo Zhu, Song Huang, Tingwen School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Australian AI Institute Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia School of Information Science and Engineering Chengdu University Chengdu611040 China School of Mathematics China University of Mining and Technology Xuzhou221116 China Science Program Texas A & M University at Qatar Doha23874 Qatar

Safety is always one of the most critical principles for a system to be controlled. This paper investigates a safety-critical control scheme for unknown structured systems by using the control barrier function (CBF) method. Benefited from the dynamic regressor extension and mixing (DREM), an extended element-wise parameter identification law is utilized to dismiss the uncertainty. On the one hand, it is shown that the proposed control scheme can always guarantee the safety in the identification process with noised signal injection excitation, which was not considered in the previous study. On the other hand, the element-wise estimation process in DREM can minimize conservatism of the safe adaptive process compared to other existing adaptive CBF algorithms. The stability as well as the forward invariance of the presented safe control-estimation scheme is proved. Furthermore, the robustness of the scheme under bounded disturbances is analyzed, where a robust CBF with modest conditions is used to ensure safety. The framework is illustrated by simulations on adaptive cruise control, where the slope resistance of the following vehicle is robustly estimated in finite time against small disturbances and the potential crash risk is avoided by the proposed safe control scheme. © 2021, CC BY-NC-ND.

关键词： Parameter estimation

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Wind energy electret-based electrostatic harvester 21

Wind energy electret-based electrostatic harvester

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21st European Conference on Power electronics and Applications, EPE 2019 ECCE Europe

作者： Coronado, Daniel Augusto Castellanos Romano, Emanuele Dallago, Enrico Juan de Castellanos University Foundation School of Engineering and Basic Sciences Research Group in Science Innovation and Technology Electronic Engineering Program Tunja Colombia University of Pavia Power Electronics Laboratory Department of Electrical Computer and Biomedical Engineering Pavia Italy

ISBN: (纸本)9789075815313

In this paper an electret-based electrostatic induction wind harvester is presented. The induction electrostatic field is produced with an electret. It is described how the electret and the harvester prototype have been obtained. A complete electrical characterization of the harvester and experimental results are then reported. © 2019 EPE Association.

关键词： Wind power

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Set-theoretic detection of data corruption attacks on cyber physical power systems

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Journal of Modern Power Systems and Clean Energy 2018年第5期6卷 872-886页

作者： Efstathios KONTOURAS Anthony TZES Leonidas DRITSAS Electrical and Computer Engineering Department University of Patras Electrical and Computer Engineering Program New York University Abu Dhabi Department of Electrical and Electronic Engineering Educators School of Pedagogical and Technological EducationASPETE 14121 Athens Greece

This paper addresses a set-theoretic method for the detection of data corruption cyber-attacks on the load frequency control loop of a networked power system. The system consists of several interconnected control areas forming a power grid. Based on the overall discrete-time network dynamics, a convex and compact polyhedral robust invariant set is extracted and is used as a set-induced anomaly detector. If the state vector exits the invariant set,then an alarm will be activated, and the potential threat is considered disclosed. The attack scenario used to assess the efficiency of the proposed anomaly detector concerns corrupted frequency sensor measurements transmitted to the automatic generation control unit of a compromised control area. Simulation studies highlight the ability of a set-theoretic approach to disclose persistent and intermittent attack patterns even when they occur at the same time with changes in the power load demand.

关键词： Power systems Load frequency control Cyber-attacks Set-theoretic methods

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Optimizing NARX-RNN Performance to Predict Precious Metal Futures market

Optimizing NARX-RNN Performance to Predict Precious Metal Fu...

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Green Energy, Computing and Sustainable Technology (GECOST), International Conference on

作者： Stephanie Dhanuskodi Rengasamy Filbert H. Juwono Jobrun Nandong Andrew J. Brennan Lenin Gopal Dept. of Acct. Finance and Economics Faculty of Business Curtin University Malaysia Miri Sarawak Malaysia Computer Science Program University of Southampton Malaysia Nusajaya Johor Malaysia Dept. of Chemical and Energy Eng. Faculty of Engineering and Science Curtin University Malaysia Miri Sarawak Malaysia Sch. of Acct. Economics and Finance Faculty of Business and Law Curtin University Bentley Perth Australia Dept. of Electrical and Electronic Eng. University of Southampton Malaysia Nusajaya Johor Malaysia

ISBN: (纸本)9781665486644

Precious metals offer lucrative investments appealing to investors globally, leading to a surge in demand for accurate forecasts. Published literature for prediction applications often employs Artificial Neural Networks (ANNs), possessing desirable generalization over nonlinear data and design flexibility. Recurrent Neural Networks (RNNs) are a class of ANNs designed for time series forecasts providing superior approximations. Nonlinear Autoregressive with Exogenous input (NARX) is an RNN model with high memory retention properties, applied in this study to predict ten assets from the precious metal futures market, for three-month predictions (April 2021-June 2021). Network inputs are evaluated through feature selection to filter uncorrelated factors from the network dataset. Accuracy of prediction is enhanced through multi-objective Response Surface Methodology (RSM) optimization, as several variables characterize RNN performance. Three key variables are selected for analysis through RSM, providing optimum configuration to obtain targeted outcome. Simulation results reveal that five assets produce acceptable result, showing an improved fitness through RSM-suggested configurations. Observations indicate intercorrelation between RSM inputs, highlighting its efficiency over conventional methods. Implementing additional RSM inputs to develop more complex models might achieve further reliability. This research provides performance improvement measures for RNNs utilized in financial data projections.

关键词： Recurrent neural networks Simulation Time series analysis Metals Predictive models Response surface methodology Reliability

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Comparison of Algebraic Reconstruction Technique Methods and Generative Adversarial Network in Image Reconstruction of Magnetic Induction Tomography (MIT)

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Journal of Physics: Conference Series 2021年第1期2071卷

作者： A J Lubis N F Mohd Nasir Z Zakaria M Jusoh M M Azizan M Z Zakaria M N F M Kazim S Rahman Faculty of Engineering and Computer Science Universitas Harapan Medan Medan Indonesia Computer Engineering Program Faculty of Electronic Engineering Technology Universiti Malaysia Perlis Perlis Malaysia Biomedical Electronic Engineering Program Faculty of Electronic Engineering Technology Universiti Malaysia Perlis Perlis Malaysia Sports Engineering Research Centre (SERC) Universiti Malaysia Perlis PerlisMalaysia Department of Electrical and Electronic Department Faculty of Engineering and Built Environment Universiti Sains Islam Malaysia Negeri Sembilan Malaysia Faculty of Mechanical Engineering Technology Universiti Malaysia Perlis Perlis Malaysia Mechatronic Engineering Program Faculty of Electrical Engineering Technology Universiti Malaysia Perlis Perlis Malaysia

Magnetic induction tomography (MIT) is a technique used for imaging electromagnetic properties of objects using eddy current effects. The non-linear characteristics had led to more difficulties with its solution especially in dealing with low conductivity imaging materials such as biological tissues. Two methods that could be applied for MIT image processing which is the Generative Adversarial Network (GAN) and the Algebraic Reconstruction Technique (ART). ART is widely used in the industry due to its ability to improve the quality of the reconstructed image at a high scanning speed. GAN is an intelligent method which would be able to carry out the training process. In the GAN method, the MIT principle is used to find the optimum global conductivity distribution and it is described as a training process and later, reconstructed by a generator. The output is an approximate reconstruction of the distribution's internal conductivity image. Then, the results were compared with the previous traditional algorithm, namely the regularization algorithm of BPNN and Tikhonov Regularization method. It turned out that GAN had able to adjust the non-linear relationship between input and output. GAN was also able to solve non-linear problems that cannot be solved in the previous traditional algorithms, namely Back Propagation Neural Network (BPNN) and Tikhonov Regularization method. There are several other intelligent algorithms such as CNN (Convolution Neural Network) and K-NN (K-Nearest Neighbor), but such algorithms have not been able to produce the expected image quality. Thus, further study is still needed for the improvement of the image quality. The expected result in this study is the comparison of these two techniques, namely ART and GAN to get the best results on the image reconstruction using MIT. Thus, it is shown that GAN is a better candidate for this purpose.

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PV System Power Model Application in Smart Cities 2

PV System Power Model Application in Smart Cities

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2nd IEEE International Conference on Knowledge Innovation and Invention, ICKII 2019

作者： Huang, Chiou-Jye Hwang, Min-Chan Chen, Hsin-Chuan Kuo, Ping-Huan School of Electrical Engineering and Automation Jiangxi University of Science and Technology Ganzhou China University of Electronic Science and Technology of China Zhongshan Institute Zhongshan China Computer and Intelligent Robot Program for Bachelor Degree National Pingtung University Pingtung Taiwan

ISBN: (纸本)9781728101101

The photovoltaic (PV) system energy generation is closely related to climate conditions, installation site, and system design. In this paper, a simple energy generation model is proposed and established by means of module temperature and solar irradiation, module temperature coefficient, module rated power, number of modules, and three correction coefficients including temperature derating coefficient, inverter conversion loss coefficient, and other loss coefficients. The experiment results show that the estimation model proposed in this paper has high accuracy using the Mean Absolute Percentage Error (MAPE) model as the evaluation index, the estimation model MAPE is 6.75%, while the PV SYST software simulation has MAPE of 11.90%. According to international MAPE evaluation standards, the method proposed in this paper has higher accuracy level than PV SYST software. This model can be used for the estimation of energy generation when installing solar PV systems, and can be used for the reasonable assessment of PV system long-term power generation, to predict system power generation anomalies, and reduce system abnormalities caused by downtime losses, and increase investment efficiency. © 2019 IEEE.

关键词： Smart city

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Correction: Advancements in additive manufacturing for video laryngoscopes: a comprehensive scoping and technological review

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Systematic reviews 2024年第1期13卷 31页

作者： Ana Cristina Beitia Kraemer Moraes Chiara das Dores do Nascimento Everton Granemann Souza Mauricio Beitia Kraemer Mauricio Moraes Neftali Lenin Villarreal Carreno Evandro Piva Rafael Guerra Lund Pelotas Dental School Graduate Program in Dentistry Federal University of Pelotas Pelotas RS 96010‑560 Brazil. Master's Degree in Electronic and Computer Engineering Center for Social and Technological Sciences Catholic University of Pelotas Pelotas RS 96015‑560 Brazil. Faculty of Medicine São Francisco University Bragança Paulista SP 12916‑900 Brazil. Faculty of Medicine Federal University of Pelotas Pelotas RS 96010‑560 Brazil. Graduate Program in Materials Science and Engineering Technological Development Center Federal University of Pelotas Pelotas RS 96010‑610 Brazil. Pelotas Dental School Graduate Program in Dentistry Federal University of Pelotas Pelotas RS 96010‑560 Brazil. rafael.lund@***. Graduate Program in Materials Science and Engineering Technological Development Center Federal University of Pelotas Pelotas RS 96010‑610 Brazil. rafael.lund@***.

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Spatio-temporal assessment of whole-body center of mass coordination on standard maximum vertical jump

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Gait & Posture 2022年 97卷 S208-S209页

作者： Rodrigues, C. Correia, M. Abrantes, J. Rodrigues, B. Nadal, J. Faculty of Engineering- University of Porto Biomedical Engineering Program Porto Portugal INESC TEC - Institute for Systems and Computer Engineering- Technology and Science C-BER - Centre for Biomedical Engineering Research Porto Portugal University of Porto Faculty of Engineering Porto Portugal Lusófona University- CICANT - Centre for Research in Applied Communication- Culture and New Technologies MovLab Laboratory of Technologies for Interactions and Interfaces Lisbon Portugal Federal University of Pernambuco Department of Electronic and Systems Recife Brazil Federal University of Rio de Janeiro Biomedical Engineering Program Rio de Janeiro Brazil

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