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SEOE:an option graph based semantically embedding method for prenatal depression detection

Frontiers of computer Science 2024年第6期18卷 245-258页

作者： Xiaosong HAN Mengchen CAO Dong XU Xiaoyue FENG Yanchun LIANG Xiaoduo LANG Renchu GUAN College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationJilin UniversityChangchun 130012China Department of Electrical Engineering and Computer Science and Christopher S.Bond Life Sciences Center University of MissouriColumbiaMO 65211USA Department of Computer Science and Technology Zhuhai Laboratory of Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationZhuhai College of Science and TechnologyZhuhai 519041China Jilin Provincial Institute of Population Science and Technology Changchun 130000China

Prenatal depression,which can affect pregnant women’s physical and psychological health and cause postpartum depression,is increasing ***,it is essential to detect prenatal depression early and conduct an attribution *** studies have used questionnaires to screen for prenatal depression,but the existing methods lack *** diagnose the early signs of prenatal depression and identify the key factors that may lead to prenatal depression from questionnaires,we present the semantically enhanced option embedding(SEOE)model to represent questionnaire *** can quantitatively determine the relationship and patterns between options and *** first quantifies options and resorts them,gathering options with little difference,since Word2Vec is highly dependent on *** resort task is transformed into an optimization problem involving the traveling salesman ***,all questionnaire samples are used to train the options’vector using ***,an LSTM and GRU fused model incorporating the cycle learning rate is constructed to detect whether a pregnant woman is suffering from *** verify the model,we compare it with other deep learning and traditional machine learning *** experiment results show that our proposed model can accurately identify pregnant women with depression and reach an F1 score of *** most relevant factors of depression found by SEOE are also verified in the *** addition,our model is of low computational complexity and strong generalization,which can be widely applied to other questionnaire analyses of psychiatric disorders.

关键词： questionnaire analyses text classification graph representation learning traveling salesman problem prenatal depression

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Intelligent Predetermination of Generator Tripping Scheme: Knowledge Fusion-based Deep Reinforcement Learning Framework

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CSEE Journal of Power and Energy Systems 2024年第1期10卷 66-75页

作者： Lingkang Zeng Wei Yao Ze Hu Hang Shuai Zhouping Li Jinyu Wen Shijie Cheng State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronics EngineeringHuazhong University of Science and TechnologyWuhan 430074China Dispatching and Control Center Central China Branch of State Grid Corporation of ChinaWuhan 430077China. Department of Electrical Engineering and Computer Science University of TennesseeKnoxvilleTN 37996USA

Generator tripping scheme(GTS)is the most commonly used scheme to prevent power systems from losing safety and ***,GTS is composed of offline predetermination and real-time scenario ***,it is extremely time-consuming and labor-intensive for manual predetermination for a large-scale modern power *** improve efficiency of predetermination,this paper proposes a framework of knowledge fusion-based deep reinforcement learning(KF-DRL)for intelligent predetermination of ***,the Markov Decision Process(MDP)for GTS problem is formulated based on transient instability ***,linear action space is developed to reduce dimensionality of action space for multiple controllable ***,KF-DRL leverages domain knowledge about GTS to mask invalid actions during the decision-making *** can enhance the efficiency and learning ***,the graph convolutional network(GCN)is introduced to the policy network for enhanced learning *** simulation results obtained on New England power system demonstrate superiority of the proposed KF-DRL framework for GTS over the purely data-driven DRL method.

关键词： Deep reinforcement learning generator tripping scheme graph convolutional network invalid action masking knowledgefusion

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Comparative Study of Parametric Disturbances Effect on Cart-Inverted Pendulum System Stabilization 1

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International Conference on Modern Electronics Devices and Communication Systems, MEDCOM 2021

作者： Verma, Neelam Valluru, Sudarshan K. Department of Electrical Engineering Center for Control of Dynamical Systems and Computation Delhi Technological University Delhi India

ISBN: (数字)9789811963834

ISBN: (纸本)9789811963827

The main motivation behind the present work was to validate the impact of pendulum mass, cart mass, and length of pendulum on stabilization and swing-up of cart-inverted pendulum. Inverted pendulum system is a classic benchmark example of underactuated electro-mechanical systems, where the degree of freedom is less than the number of inputs available. PID controller was designed for a set of parameter values of the system. MATLAB simulation was carried out for different values of pendulum mass (0.2–0.25 kg), pendulum length (0.3–0.34 m), and cart mass (2.3–2.45 kg). The performance of considered cart- inverted pendulum system is demonstrated with simulation results for a different set of results, and a comparative analysis was made to comment on their effects on swing-up and stabilization. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Stabilization

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An Improved Method for Approximating the Infinite-Horizon Value Function of the Discrete-Time Switched LQR Problem

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Journal of Systems Science & Complexity 2024年第1期37卷 22-39页

作者： HOU Tan LI Yuanlong LIN Zongli Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai 200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China Charles L.Brown Department of Electrical and Computer Engineering University of VirginiaCharlottesvilleVA 22904-4743USA

This paper considers the problem of approximating the infinite-horizon value function of the discrete-time switched LQR *** particular,the authors propose a new value iteration method to generate a sequence of monotonically decreasing functions that converges exponentially to the value *** method facilitates us to use coarse approximations resulting from faster but less accurate algorithms for further value iteration,and thus,the proposed approach is capable of achieving a better approximation for a given computation time compared with the existing *** numerical examples are presented in this paper to illustrate the effectiveness of the proposed method.

关键词： Optimal control switched linear systems switched LQR problem.

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Correction of Time-varying PMU Phase Angle Deviation with Unknown Transmission Line Parameters

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CSEE Journal of Power and Energy Systems 2023年第1期9卷 315-325页

作者： Ancheng Xue He Kong Feiyang Xu Junbo Zhao Naichao Chang Joe H.Chow Haiyan Hong State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China Department of Electrical and Computer Engineering University of ConnecticutStorrs06268USA National Electric Power Dispatching and Control Center Beijing 100031China Department of Electrical Computerand Systems EngineeringRensselaer Polytechnic Institute(RPI)TroyNY 12180USA

Phasor measurement units(PMUs)provide useful data for real-time monitoring of the smart ***,there may be time-varying deviation in phase angle differences(PADs)between both ends of the transmission line(TL),which may deteriorate application performance based on *** address that,this paper proposes two robust methods of correcting time-varying PAD deviation with unknown parameters of TL(ParTL).First,the phenomena of time-varying PAD deviation observed from field PMU data are *** general formulations for PAD estimation are then *** simplify the formulations,estimation of PADs is converted into the optimal problem with a single ParTL as the variable,yielding a linear estimation of *** latter is used by second-order Taylor series expansion to estimate PADs *** reduce the impact of possible abnormal amplitude data in field data,the IGG(Institute of Geodesy&Geophysics,Chinese Academy of Sciences)weighting function is *** using both simulated and field data verify the effectiveness and robustness of the proposed methods.

关键词： Correction line parameters parameter identification phasor measurement time-varying phase angle difference deviation

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A Modified Dempster Shafer Approach to Classification in Surgical Skill Assessment 31

A Modified Dempster Shafer Approach to Classification in Sur...

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31st International Conference on electrical engineering, ICEE 2023

作者： Iranfar, Arash Soleymannejad, Mohammad Moshiri, Behzad Taghirad, Hamid D. School of Electrical and Computer Engineering University College of Engineering University of Tehran Tehran Iran University of Waterloo Department of Electrical and Computer Engineering Waterloo Canada Industrial Control Center of Excellence Faculty of Electrical Engineering Tehran Iran

ISBN: (纸本)9798350312560

Artificial intelligence systems are usually implemented either using machine learning or expert systems. Machine learning methods are usually more accurate and applicable to a broader range of applications. Expert systems, on the other hand, require much less data for training and generate more comprehensible results. These characteristics are typically desired in the fields of surgery and medicine because there isn't much data available. In order to give a machine's decisions a deeper level of semantics, it is also advantageous to incorporate a doctor's expertise into it. Furthermore, it is safer to understand the reasoning behind a machine's choices. In this paper, a Dempster-Shafer Theory (DST) based expert system is suggested for the task of surgical training skill assessment. An interval-based probabilistic feature analysis was applied to the data to assign values to the mass functions. Zhang's rule of combination was applied to handle the conflicting evidence in the prediction phase. The performance of the proposed method was compared to another DST classifier, SVM, and XGBoost. Our method outperforms SVM and other DST classifiers, but it is not as precise as XGBoost. By reducing the size of the dataset, the added benefit of using an expert system as opposed to a machine learning method was explored further. The performance of the suggested method is not adversely affected by the size of the dataset, whereas the XGBoost classifier is. © 2023 IEEE.

关键词： Classification (of information)

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On the chronological understanding of the homogeneous dielectric barrier discharge

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High Voltage 2023年第6期8卷 1132-1150页

作者： Xinpei Lu Zhi Fang Dong Dai Tao Shao Feng Liu Cheng Zhang Dawei Liu Lanlan Nie Chunqi Jiang State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhanChina College of Electrical Engineering and Control Science Nanjing Tech TechnologyNanjingChina School of Electric Power South China University of TechnologyGuangzhouChina Institute of Electrical Engineering Chinese Academy of SciencesBeijingChina Frank Reidy Research Center for Bioelectrics Old Dominion UniversityNorfolkVirginiaUSA Department of Electrical and Computer Engineering Old Dominion UniversityNorfolkVirginiaUSA

Dielectric barrier discharges(DBD)are widely utilised non‐equilibrium atmospheric pressure plasmas with a diverse range of applications,such as material processing,surface treatment,light sources,pollution control,and *** the course of several decades,extensive research has been dedicated to the generation of homogeneous DBD(H‐DBD),focussing on understanding the transition from H‐DBD to filamentary DBD and exploring strategies to create and sustain H‐*** paper first discusses the in-fluence of various parameters on DBD,including gas flow,dielectric material,surface conductivity,and mesh ***,a chronological literature review is presented,highlighting the development of H‐DBD and the associated understanding of its un-derlying *** encompasses the generation of H‐DBD in helium,nitrogen,and ***,the paper provides a brief overview of multiple‐current‐pulse(MCP)behaviours in H‐*** objective of this article is to provide a chronological un-derstanding of homogeneous dielectric barrier discharge(DBD).This understanding will aid in the design of new experiments aimed at better comprehending the mechanisms behind H‐DBD generation and ultimately assist in achieving large‐volume H‐DBD in an air environment.

关键词： dielectric barrier pollution

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Hamiltonian quantum generative adversarial networks

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Physical Review Research 2024年第3期6卷 033019-033019页

作者： Leeseok Kim Seth Lloyd Milad Marvian Department of Electrical & Computer Engineering and Center for Quantum Information and Control Department of Mechanical Engineering

We propose Hamiltonian quantum generative adversarial networks (HQuGANs) to learn to generate unknown input quantum states using two competing quantum optimal controls. The game-theoretic framework of the algorithm is inspired by the success of classical generative adversarial networks in learning high-dimensional distributions. The quantum optimal control approach not only makes the algorithm naturally adaptable to the experimental constraints of near-term hardware, but also offers a more natural characterization of overparameterization compared to the circuit model. We numerically demonstrate the capabilities of the proposed framework to learn various highly entangled many-body quantum states, using simple two-body Hamiltonians and under experimentally relevant constraints such as low-bandwidth controls. We analyze the computational cost of implementing HQuGANs on quantum computers and show how the framework can be extended to learn quantum dynamics. Furthermore, we introduce a cost function that circumvents the problem of mode collapse that prevents convergence of HQuGANs and demonstrate how to accelerate the convergence of them when generating a pure state.

关键词： Machine learning Quantum algorithms Quantum computation Quantum control

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Interpretation of ADRC in Q-Structure 6

Interpretation of ADRC in Q-Structure

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6th International Conference on Industrial Artificial Intelligence, IAI 2024

作者： Xu, Jiapeng Chen, Xiang Tan, Ying Zhou, Kemin University of Windsor Department of Electrical and Computer Engineering Windsor Canada The University of Melbourne Department of Mechanical Engineering Melbourne Australia Center for Advanced Control and Smart Operations Nanjing University Suzhou China

ISBN: (纸本)9798350356618

This paper presents a control structure featuring an operator Q driven by the residual signal, which indicates the difference between the measurement output and the estimated output from an observer. The form of this observer is very general, as long as it can generate the estimated state or relevant information that will be incorporated into this control structure. The operator Q introduces an extra design freedom to address uncertainties, such that linear active disturbance rejection control (LADRC) can be interpreted as a special case in the Q-structure when a linear extended state observer is used. © 2024 IEEE.

关键词： Disturbance rejection

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Microgrid Energy Management with Energy Storage Systems:A Review

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CSEE Journal of Power and Energy Systems 2023年第2期9卷 483-504页

作者： Xiong Liu Tianyang Zhao Hui Deng Peng Wang Jizhen Liu Frede Blaabjerg Energy Electricity Research Center Jinan UniversityZhuhai 519070China School of Electrical and Electronic Engineering Nanyang Technological University639798 Singapore School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China Department of Energy Technology at Aalborg University Denmark

Microgrids(MGs)are playing a fundamental role in the transition of energy systems towards a low carbon future due to the advantages of a highly efficient network architecture for flexible integration of various DC/AC loads,distributed renewable energy sources,and energy storage systems,as well as a more resilient and economical on/off-grid control,operation,and energy ***,MGs,as newcomers to the utility grid,are also facing challenges due to economic deregulation of energy systems,restructuring of generation,and marketbased *** paper comprehensively summarizes the published research works in the areas of MGs and related energy management modelling and solution ***,MGs and energy storage systems are classified into multiple branches and typical combinations as the backbone of MG energy ***,energy management models under exogenous and endogenous uncertainties are summarized and extended to transactive energy *** programming,adaptive dynamic programming,and deep reinforcement learning-based solution methods are investigated accordingly,together with their implementation ***,problems for future energy management systems with dynamics-captured critical component models,stability constraints,resilience awareness,market operation,and emerging computational techniques are discussed.

关键词： Architecture energy management energy storage systems microgrids optimization uncertainty models

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