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IEEE International Conference on control Science and systems engineering (CCSSE)

作者： Zhimin Gao Yongji Cao Hengxu Zhang Hao Qin Xiaoning Zhang Changgang Li Key Laboratory of Power System Intelligent Dispatch and Control of the Ministry of Education Shandong University Jinan China Department of Electrical Engineering Technical University of Denmark Kgs. Lyngby Denmark School of Control and Computer Engineering North China Electric Power University Beijing China

The large-scale integration of renewable energies brings about low inertia characteristics to the power system, and the frequency security faces greater challenges. The existing inertia research lacks the synergy between different types of inertia sources and different control methods, and the inertia response capability of power system still can be improved. In this paper, with the aim of proposing a virtual inertia cooperative control strategy for the power system with high penetration of renewable energies, we analyze the virtual inertia mechanisms of wind power, PV units and energy storage system (ESS), and propose control methods for wind power and PV units to participate in inertia response. Considering the reason of limited inertia response capability of renewable energy units, the fuzzy control is used to make the ESS participate in inertia compensation to improve the frequency response capability. Finally, case study is carried out to verified the effectiveness of the proposed control strategy.

关键词： Renewable energy sources Fuzzy control Rotors Wind power generation Power system stability Synchronization Security

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Quantitative Inertia Estimation Method for Wind Farm Based on AFF-RLS

Quantitative Inertia Estimation Method for Wind Farm Based o...

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IEEE International Conference on control Science and systems engineering (CCSSE)

作者： Hao Qin Yongji Cao Hengxu Zhang Zhimin Gao Xiaoning Zhang Changgang Li Key Laboratory of Power System Intelligent Dispatch and Control of the Ministry of Education Shandong University Jinan China Department of Electrical Engineering Technical University of Denmark Kgs. Lyngby Denmark School of Control and Computer Engineering North China Electric Power University Beijing China

In order to guarantee the frequency security and stability of the power system with high proportional of wind generation, virtual inertia control techniques are gradually being promoted and applied. However, there is still a lack of methods and techniques to accurately estimate the equivalent virtual inertia of wind farms in real time, which makes it difficult to quantitatively evaluate the effect of virtual inertia control and to dynamically monitor the inertia level of power systems. Therefore, in this paper, an AR model with eXogenous input is applied to the time-varying characteristics of the virtual inertia of wind farm, and a recursive least squares identification algorithm based on a fuzzy control algorithm with an adaptive forgetting factor is proposed to estimate the virtual inertia of wind farms online. The proposed identification model and solution algorithm have superior tracking ability and good convergence. All that is required is to obtain perturbation data on the frequency and active power of the point of common coupling from the phasor measurement unit. A case study is carried out to verify the effectiveness of the proposed method.

关键词： Adaptation models Heuristic algorithms Estimation Wind farms Power system stability Wind power generation Stability analysis

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Thermostat Interactions and Human Discomfort: Uncovering Spatiotemporal Variabilities in a Longitudinal Study of Residential Buildings

arXiv

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arXiv 2023年

作者： Kang, SungKu Pathak, Maharshi Sharma, Kunind Casavant, Emily Bassett, Katherine Pavel, Misha Fannon, David Kane, Michael Korea Aerospace University Department of Mechanical and Aircraft System Engineering Gyeonggido Goyang-si10540 Korea Republic of Northeastern University Civil and Environmental Engineering Department BostonMA02115 United States Northeastern University Khoury College of Computer Sciences BostonMA02215 United States Northeastern University School of Architecture BostonMA02215 United States

Growing variable renewable energy and electrification of heating and transportation are intensifying the challenge of operating the electric grid. However, current demand response (DR) approaches compromise their efficacy by neglecting human-building interactions (HBIs). For example, utilities may increase thermostat setpoints on the hottest days of the year, reducing the strain on the grid but making occupants uncomfortable and frustrated. To better understand HBIs in residential buildings, 41 people in 20 homes in two climates participated in a 6-month study. Timestamps from app-based thermal comfort surveys and thermostat interactions were synchronized to time-series building systems data, resulting in the largest-of-its-kind HBI dataset. These survey data are compared to predictions from industry-standard thermal comfort models. Statistically significant results show that these models, developed under steady-state assumptions, yield greater error magnitudes and/or biases when spatiotemporal temperature variations exceed 2°F. The mean spatial variation within homes in the dataset was 4°F, thermostat DR control would commonly exacerbate such temporal variation. The results highlight opportunities for improving DR load-control algorithms through a paradigm shift to modeling discomfort rather than comfort, increasing the use of low-cost sensors, and incorporating dynamic models of occupant behavior. © 2023, CC BY-NC-ND.

关键词： Demand response

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Unlocking the Secrets Behind Advanced Artificial Intelligence Language Models in Deidentifying Chinese-English Mixed Clinical Text: Development and Validation Study

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Journal of Medical Internet Research 2024年第1期26卷 e48443页

作者： Lee, You-Qian Chen, Ching-Tai Chen, Chien-Chang Lee, Chung-Hong Chen, Peitsz Wu, Chi-Shin Dai, Hong-Jie Dialogue System Technical Department Asustek Computer Inc Taipei Taiwan Intelligent System Laboratory Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Bioinformatics and Medical Engineering Asia University Taichung Taiwan Center for Precision Health Research Asia University Taichung Taiwan Electromagnetic Sensing Control and AI Computing System Laboratory Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Knowledge Discovery and Data Mining Lab Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Chemical Engineering Feng Chia University Taichung Taiwan National Center for Geriatrics and Welfare Research National Health Research Institutes Zhunan Taiwan National Institute of Cancer Research National Health Research Institutes Tainan Taiwan School of Post-Baccalaureate Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Center for Big Data Research Kaohsiung Medical University Kaohsiung Taiwan

Background: The widespread use of electronic health records in the clinical and biomedical fields makes the removal of protected health information (PHI) essential to maintain privacy. However, a significant portion of information is recorded in unstructured textual forms, posing a challenge for deidentification. In multilingual countries, medical records could be written in a mixture of more than one language, referred to as code mixing. Most current clinical natural language processing techniques are designed for monolingual text, and there is a need to address the deidentification of code-mixed text. Objective: The aim of this study was to investigate the effectiveness and underlying mechanism of fine-tuned pretrained language models (PLMs) in identifying PHI in the code-mixed context. Additionally, we aimed to evaluate the potential of prompting large language models (LLMs) for recognizing PHI in a zero-shot manner. Methods: We compiled the first clinical code-mixed deidentification data set consisting of text written in Chinese and English. We explored the effectiveness of fine-tuned PLMs for recognizing PHI in code-mixed content, with a focus on whether PLMs exploit naming regularity and mention coverage to achieve superior performance, by probing the developed models’ outputs to examine their decision-making process. Furthermore, we investigated the potential of prompt-based in-context learning of LLMs for recognizing PHI in code-mixed text. Results: The developed methods were evaluated on a code-mixed deidentification corpus of 1700 discharge summaries. We observed that different PHI types had preferences in their occurrences within the different types of language-mixed sentences, and PLMs could effectively recognize PHI by exploiting the learned name regularity. However, the models may exhibit suboptimal results when regularity is weak or mentions contain unknown words that the representations cannot generate well. We also found that the availability of cod

关键词： ChatGPT code mixing deidentification electronic health record large language model pretrained language model

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Multi-kernel Correntropy-based Orientation Estimation of IMUs: Gradient Descent Methods

arXiv

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arXiv 2023年

作者： Li, Shilei Li, Lijing Shi, Dawei Lou, Yunjiang Shi, Ling The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong The School of Information and Control Engineering China University of Mining and Technology China The School of Automation Beijing Institute of Technology China The State Key Laboratory of Robotics and System School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen518055 China

This paper presents two computationally efficient algorithms for the orientation estimation of inertial measurement units (IMUs): the multi-kernel correntropy-based gradient descent (CGD) and the multi-kernel correntropy-based decoupled orientation estimation (CDOE). Traditional methods, such as gradient descent (GD) and decoupled orientation estimation (DOE), rely on the least square (LS) criterion in algorithm derivation, making them vulnerable to external acceleration and magnetic interference. To address this issue, we first demonstrate that the multi-kernel correntropy loss (MKCL) is an optimal objective function under the maximum likelihood estimation (MLE) framework when the noise follows a specific type of heavy-tailed distribution. Then, we provide some important properties of the MKCL as a cost function. By replacing the LS cost with the MKCL, we develop the CGD and CDOE algorithms. We evaluate the effectiveness of our proposed methods by comparing them with existing algorithms in various situations. Experimental results indicate that our proposed methods (CGD and CDOE) outperform their conventional counterparts (GD and DOE), especially when faced with external acceleration and magnetic disturbances. Furthermore, the new algorithms demonstrate significantly lower computational complexity than Kalman filter-based approaches, making them suitable for applications with low-cost microprocessors. Copyright © 2023, The Authors. All rights reserved.

关键词： Maximum likelihood estimation

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Integral Inequalities for the Analysis of Distributed Parameter systems: A Complete Characterization via the Least-Squares Principle

Integral Inequalities for the Analysis of Distributed Parame...

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American control Conference (ACC)

作者： Qian Feng Alexandre Seuret Sing Kiong Nguang Feng Xiao School of Control and Computer Engineering North China Electric Power University Beijing China Dpto de Ingeniería de Sistemas y Automática Universidad de Sevilla Camino de los Descubrimientos Sevilla Spain Department of Electrical Computer and Software Engineering The University of Auckland New Zealand State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing China

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

A wide variety of integral inequalities (IIs) have been developed and studied for the stability analysis of distributed parameter systems using the Lyapunov functional approach. However, no unified mathematical framework has been proposed that could characterize the similarity and connection between these IIs, as most of them was introduced in a dispersed manner for the analysis of specific types of systems. Additionally, the extent to which the generality of these IIs can be expanded and the optimality of their lower bounds (LBs) remains open questions. In this study, we introduce two general classes of IIs that can generalize nearly all IIs in the literature. The integral kernels of the LBs of our IIs can contain an unlimited number of weighted £2 functions that are linearly independent in a Lebesgue sense. Moreover, we not only estalish the equivalence relations between the LBs of our IIs, but also demonstrate that these LBs are guaranteed by the least squares principle, implying asymptotic convergence to the upper bound when the kernels functions constitutes a Schauder basis of the underlying Hilbert space. Owing to their general structures, our IIs are applicable in a variety of contexts, such as the stability analysis of coupled PDE-ODE systems or cybernetic systems with delay structures.

关键词： Upper bound Integral equations Stability analysis Hilbert space Linear matrix inequalities Delays Kernel

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Platoon-Based Distributed control for Automated Material Handling systems

Platoon-Based Distributed Control for Automated Material Han...

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International Conference on control, Decision and Information Technologies (CoDIT)

作者： Alessandro Bozzi Jose-Fernando Jimenez Camilo Hernandez-Rodriguez Eliana-Maria Gonzalez-Neira Damien Trentesaux Department of Informatics Bioengineering Robotics and Systems Engineering (DIBRIS) University of Genova Italy Laboratory System and Materials for Mechatronics (SYMME) Université Savoie Mont Blanc Annecy France Industrial Engineering Department Pontificia Universidad Javeriana Bogota Columbia Laboratory of Industrial and Human Automation Control Mechanical Engineering and Computer Science (LAMIH) Université Polytechnique Hauts-de-France Valenciennes France

Smart manufacturing systems combine collaborative and autonomous control systems to provide optimal performance and respond to volatile demands. A key component of smart manufacturing is material management, as raw materials, work-in-progress, and finished products must be transported to the right location at the right time. However, during the production phase, it is imperative to establish an optimal traffic control scheme in order to avoid congestion and optimize material flow. This paper proposes a platooning-based control procedure implemented in a virtual agent-based manufacturing environment to increase the material flow efficiency on a manufacturing shop floor. An analysis of the production makespan and an indicator of the level of congestion in the material handling system was conducted in order to determine productivity improvements. An agent-based simulation is used to test the effectiveness of the novel platooning approach versus a traditional basic approach. A multivariate analysis of variance (MANOVA) reveals that the inclusion of a platoon-based procedure improves production performance and reduces congestion risks.

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Isoform Function Prediction Based on Heterogeneous Graph Attention Networks

Isoform Function Prediction Based on Heterogeneous Graph Att...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Kuo Guo Yifan Li Hao Chen Hong-Bin Shen Yang Yang Department of Computer Science and Engineering Shanghai Jiao Tong University and Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Computational Biology Department School of Computer Science Carnegie Mellon University Pittsburgh PA USA

Isoforms refer to different mRNA molecules transcribed from the same gene, which can be translated into proteins with varying structures and functions. Predicting the functions of isoforms is an essential topic in bioinformatics as it can provide valuable insights into the intricate mechanisms of gene regulation and biological processes. Conventionally, gene function labels are standardized in Gene Ontology (GO) terms. However, traditional methods for predicting isoform function are largely limited by the absence of isoform-specific labels, sparse annotations, and the vast number of GO terms. To address these issues, we propose HANIso, a deep learning-based method for isoform function prediction. HANIso leverages a pretrained protein language model to extract features from protein sequences. It also integrates heterogeneous information, such as isoform sequence features, GO annotations, and isoform interaction data, using a Heterogeneous Graph Attention Network (HAN). This allows the model to learn the importance of different sources of information and their semantic relationships through the attention mechanism. Our method can predict function labels at both the gene level and isoform level. We conduct experiments on two species datasets, and the results demonstrate that our method outperforms existing methods on both AUROC and AUPRC. HANIso has the potential to overcome the limitations of traditional methods and provide a more accurate and comprehensive understanding of isoform function.

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Multi-kernel Correntropy Regression: Robustness, Optimality, and Application on Magnetometer Calibration

arXiv

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arXiv 2023年

作者： Li, Shilei Chen, Yihan Lou, Yunjiang Shi, Dawei Li, Lijing Shi, Ling The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong The State Key Laboratory of Robotics and System School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen518055 China The School of Automation Beijing Institute of Technology China The School of Information and Control Engineering China University of Mining and Technology China

This paper investigates the robustness and optimality of the multi-kernel correntropy (MKC) on linear regression. We first derive an upper error bound for a scalar regression problem in the presence of arbitrarily large outliers and state that the kernel bandwidth plays an important role in minimizing the lowest upper error bound. Then, we find that the proposed MKC is related to a specific heavy-tail distribution, where its head shape is consistent with the Gaussian distribution but its tail shape is heavy-tailed, and the extent of heavy-tail is controlled by the kernel bandwidth. It becomes a Gaussian distribution when the bandwidth is infinite, which allows one to tackle both Gaussian and non-Gaussian problems without explicitly investigating the noise distributions. To explore the optimal underlying distribution parameters, an expectation-maximization-like (EM) algorithm is developed to estimate the parameter vectors and the distribution parameters in an alternating manner. The results show that our algorithm can achieve equivalent performance compared with the traditional linear regression under Gaussian noise, and it significantly outperforms the conventional method under heavy-tailed noise. Both numerical simulations and experiments on a magnetometer calibration application verify the effectiveness of the proposed method. Note to Practitioners-The goal of this paper is to enhance the accuracy of conventional linear regression in handling outliers while maintaining its optimality under Gaussian assumptions. Our algorithm is formulated under the maximum likelihood estimation (MLE) framework, assuming the regression residuals follow a type of heavy-tailed noise distribution with an extreme case of Gaussian. The degree of the heavy tail is explored alternatingly using an Expectation-Maximization (EM) algorithm which converges very quickly. The robustness and optimality of the proposed approach are investigated and compared with the traditional approaches. Both th

关键词： Linear regression

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Dynamic time warping-based transfer learning for improving common spatial patterns in brain-computer interface

Dynamic time warping-based transfer learning for improving c...

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作者： Azab, Ahmed M. Ahmadi, Hamed Mihaylova, Lyudmila Arvaneh, Mahnaz Department of Automatic Control and System Engineering Sheffield University Sheffield United Kingdom School of Computer Science and Electronic Engineering University of Essex Essex United Kingdom

Objective. Common spatial patterns (CSP) is a prominent feature extraction algorithm in motor imagery (MI)-based brain-computer interfaces (BCIs). However, CSP is computed using sample-based covariance-matrix estimation. Hence, its performance deteriorates if the number of training trials is small. To address this problem, this paper proposes a novel regularized covariance matrix estimation framework for CSP (i.e. DTW-RCSP) based on dynamic time warping (DTW) and transfer learning. Approach. The proposed framework combines the subject-specific covariance matrix (Σ55) estimated using the available trials from the new subject, with a novel DTW-based transferred covariance matrix (ΣDTW) estimated using previous subjects trials. In the proposed ΣDTW, the available labelled trials from the previous subjects are temporally aligned to the average of the available trials of the new subject from the same class using DTW. This alignment aims to reduce temporal variations and non-stationarities between previous subjects' trials and the available trials from the new subjects. Moreover, to tackle the problem of regularization parameter selection when only a few trials are available for training, an online method is proposed, where the best regularization parameter is selected based on the confidence scores of the trained classifier on the upcoming first few labelled testing trials. Main results. The proposed framework is evaluated on two datasets against two baseline algorithms. The obtained results reveal that DTW-RCSP significantly outperformed the baseline algorithms at various testing scenarios, particularly, when only a few trials are available for training. Significance. Impressively, our results show that successful BCI interactions could be achieved with a calibration session as small as only one trial per class. © 2020 IOP Publishing Ltd.

关键词： Covariance matrix

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