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Optimal Operation of an Integrated Electricity-heat Energy System Considering Flexible Resources Dispatch for Renewable Integration

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Journal of Modern Power systems and Clean Energy 2021年第4期9卷 699-710页

作者： Wei Wang Shuhao Huang Guangming Zhang Jizhen Liu Zhe Chen State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China Department of Energy Technology Aalborg University9220 AalborgDenmark

Large fluctuations may occur on the energy supply and the load sides when large-scale renewable energies are integrated,leading to great challenges in power *** renewable power curtailment is especially numerous in the integrated electricity-heat energy system(IEHES)on account of electricity-heat *** flexible resources(FRs)on both the energy supply and load sides are introduced into the optimal dispatch of the IEHES and further modeled to alleviate the renewable fluctuations in this *** the energy supply side,three kinds of FRs based on electricity-heat coordination are modeled and *** the load side,the shiftable electricity demand resource is *** this basis,the solution for FRs participating in IEHES dispatch is given,with goals of maximizing the renewable penetration ratio and lowering operation *** scenarios are performed,and the results indicate that the proposed optimal dispatch strategy can effectively reduce the renewable energy curtailment and improve the flexibility of the *** contribution degrees of different FRs for renewable integration are also explored.

关键词： Integrated electricity-heat energy system(IEHES) renewable penetration flexible resource(FR) electricity-heat coordination shiftable demand load

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Machine learning predicts long-term mortality after acute myocardial infarction using systolic time intervals and routinely collected clinical data

arXiv

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arXiv 2024年

作者： Roodini, Bijan Khajehpiri, Boshra Moghaddam, Hamid Abrishami Forouzanfar, Mohamad Department of Systems Engineering École de technologie supérieure Université du Québec MontréalQC Canada Laboratory Faculty of Electrical and Computer Engineering K. N. Toosi University of Technology Tehran Iran Montréal Canada

Background: Precise estimation of cardiac patients’ current and future comorbidities is an important factor in prioritizing continuous physiological monitoring and new therapies. Machine learning (ML) models have shown satisfactory performance in short-term mortality prediction of patients with heart disease, while their utility in long-term predictions is limited. This study aims to investigate the performance of tree-based ML models on long-term mortality prediction and the effect of two recently introduced biomarkers on long-term mortality. Methods: This study utilized publicly available data from the Collaboration Center of Health Information Application (CCHIA) at the Ministry of Health and Welfare, Taiwan, China1. The data pertained to patients admitted to the cardiac care unit for acute myocardial infarction (AMI) between November 2003 and September 2004. Included in the dataset were patients over 20 years old diagnosed with type 1 AMI. Exclusion criteria encompassed patients with missing data for brachial pre-ejection period (bPEP) and brachial ejection time (bET), as well as those with atrial fibrillation or extremity amputations, resulting in a final cohort of 139 AMI patients. We collected and analyzed mortality data up to December 2018. All patients had provided informed consent, in accordance with the Declaration of Helsinki. Medical records were used to gather demographic and clinical data, including age, gender, body mass index (BMI), percutaneous coronary intervention (PCI) status, and comorbidities such as hypertension, dyslipidemia, ST-segment elevation myocardial infarction (STEMI), and non-ST segment elevation myocardial infarction (NSTEMI). Using medical and demographic records as well as two recently introduced biomarkers, bPEP and bET, collected from 139 patients with acute myocardial infarction, we investigated the performance of advanced ensemble tree-based ML algorithms (random forest, AdaBoost, and XGBoost) to predict all-cause mortality w

关键词： Adaptive boosting

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Analytical solution of two-conductor transmission lines excited by external electromagnetic field 28

Analytical solution of two-conductor transmission lines exci...

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28th Iranian Conference on electrical engineering, ICEE 2020

作者： Khatami, M.S. Rashed Mohassel, J.A. School of Electrical and Computer Engineering University of Tehran Tehran Iran Center of Excellence on Applied Electromagnetics Systems School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781728172965

In this paper, an analytical approach is presented to calculate the field-to-transmission line coupling. Specifically, Green functions of the telegraphic equations are presented for a simple case of two transmission lines. Then, unknown coefficients in the Green functions are represented in matrix forms to be solved by a MATLAB code. Finally, the results are compared with those of Finite Different Time Domain method and an excellent agreement is shown. © 2020 IEEE.

关键词： Electric lines

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Learning-Based Sustainable Multi-User Computation Offloading for Mobile Edge-Quantum Computing

Learning-Based Sustainable Multi-User Computation Offloading...

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IEEE International Conference on Communications (ICC)

作者： Minrui Xu Dusit Niyato Jiawen Kang Zehui Xiong Mingzhe Chen School of Computer Science and Engineering Nanyang Technological University Singapore School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Singapore Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA

In this paper, a novel paradigm of mobile edgequantum computing (MEQC) is proposed, which brings quantum computing capacities to mobile edge networks that are closer to mobile users (i.e., edge devices). First, we propose an MEQC system model where mobile users can offload computational tasks to scalable quantum computers via edge servers with cryogenic components and fault-tolerant schemes. Second, we show that it is NP-hard to obtain a centralized solution to the partial offloading problem in MEQC in terms of the optimal latency and energy cost of classical and quantum computing. Third, we propose a multi-agent hybrid discrete-continuous deep reinforcement learning using proximal policy optimization to learn the long-term sustainable offloading strategy without prior knowledge. Finally, experimental results demonstrate that the proposed algorithm can reduce at least 30% of the cost compared with the existing baseline solutions under different system settings.

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CoG-DQA: Chain-of-Guiding Learning with Large Language Models for Diagram Question Answering

CoG-DQA: Chain-of-Guiding Learning with Large Language Model...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Shaowei Wang Lingling Zhang Longji Zhu Tao Qin Kim-Hui Yap Xinyu Zhang Jun Liu School of Computer Science and Technology Xi'an Jiaotong University China Key Laboratory of Intelligent Networks and Network Security Xi'an Jiaotong University Ministry of Education Xi'an Shaanxi China School of Electrical & Electronic Engineering Nanyang Technological University Singapore

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Diagram Question Answering (DQA) is a challenging task, requiring models to answer natural language questions based on visual diagram contexts. It serves as a crucial basis for academic tutoring, technical support, and more practical applications. DQA poses significant challenges, such as the demand for domain-specific knowledge and the scarcity of annotated data, which restrict the applicability of large-scale deep models. Previous approaches have explored external knowledge integration through pretraining, but these methods are costly and can be limited by domain disparities. While Large Language Models (LLMs) show promise in question-answering, there is still a gap in how to cooperate and interact with the diagram parsing process. In this paper, we introduce the Chain-of-Guiding Learning Model for Diagram Question Answering (CoG-DQA), a novel framework that effectively addresses DQA challenges. CoG-DQA leverages LLMs to guide diagram parsing tools (DPTs) through the guiding chains, enhancing the precision of diagram parsing while introducing rich background knowledge. Our experimental findings reveal that CoG-DQA surpasses all comparison models in various DQA scenarios, achieving an average accuracy enhancement exceeding 5% and peaking at 11% across four datasets. These results underscore CoG-DQA's capacity to advance the field of visual question answering and promote the integration of LLMs into specialized domains.

关键词： Bridges Visualization computer vision Large language models Computational modeling Natural languages Question answering (information retrieval)

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Leveraging Deep Reinforcement Learning for Geolocation-based MIMO Transmission in FD-RAN

Leveraging Deep Reinforcement Learning for Geolocation-based...

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IEEE International Conference on Communications in China (ICCC)

作者： Zongxi Liu Kai Yu Tianqi Zhang Jingbo Liu Jiacheng Chen Haibo Zhou Xuemin Sherman Shen School of Electronic Science and Engineering Nanjing University Nanjing China Department of Mathematics and Theories Peng Cheng Laboratory Shenzhen China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada

In the fully-decoupled radio access network (FD-RAN), the base stations (BSs) are classified as control BSs, uplink BSs, and downlink BSs for better flexibility of the BS-user equipment (UE) association and elasticity of resource scheduling. However, the high feedback delay brought by the physically decoupling architecture renders conventional feedback-based MIMO transmission ineffective. Considering the strong correlation between user geolocation and the channel propagation environment, especially when a line-of-sight (LOS) path exists, we propose a geolocation-based MIMO transmission scheme that determines the user’s precoder and modulation and coding scheme (MCS) without requiring the conventional feedback procedures. To capture the correlation, we utilize deep reinforcement learning (DRL) to perform the task of mapping from geolocation to transmission parameters. Based on the principles of Type I codebook, we further decompose the task into subtasks to reduce the action space. The extensive simulations on realistic ray-tracing channels demonstrate comparable performance to CSI feedback-based methods in 5G under ideal (zero feedback delay) conditions and better performance against 5G (with feedback delay) and DRL without task decomposition.

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Should Audio Front-ends be Adaptive? Comparing Learnable and Adaptive Front-ends

arXiv

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arXiv 2025年

作者： Zhang, Qiquan Wickramasinghe, Buddhi Ambikairajah, Eliathamby Sethu, Vidhyasaharan Li, Haizhou School of Electrical Engineering and Telecommunications The University of New South Wales Sydney2052 Australia School of Electrical and Computer Engineering Purdue University West LafayetteIN United States Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong Shenzhen518172 China Shenzhen Research Institute of Big Data Shenzhen51872 China

Hand-crafted features, such as Mel-filterbanks, have traditionally been the choice for many audio processing applications. Recently, there has been a growing interest in learnable front-ends that extract representations directly from the raw audio waveform. However, both hand-crafted filterbanks and current learnable front-ends lead to fixed computation graphs at inference time, failing to dynamically adapt to varying acoustic environments, a key feature of human auditory systems. To this end, we explore the question of whether audio front-ends should be adaptive by comparing the Ada-FE front-end (a recently developed adaptive front-end that employs a neural adaptive feedback controller to dynamically adjust the Q-factors of its spectral decomposition filters) to established learnable front-ends. Specifically, we systematically investigate learnable front-ends and Ada-FE across two commonly used back-end backbones and a wide range of audio benchmarks including speech, sound event, and music. The comprehensive results show that our Ada-FE outperforms advanced learnable front-ends, and more importantly, it exhibits impressive stability or robustness on test samples over various training epochs. Copyright © 2025, The Authors. All rights reserved.

关键词： Gabor filters

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A SINS Error Correction Approach Based on Dual-Threshold ZV Detection and Cubature Kalman Filter

A SINS Error Correction Approach Based on Dual-Threshold ZV ...

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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Xu, Ruijie Chen, Shichao Sun, Wenqiao Lv, Yisheng Luo, Jialiang Tang, Ying Institute of Automation Chinese Academy of Sciences College of Information Science & Technology Beijing University of Chemical Technology The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Institute of Automation Chinese Academy of Sciences The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Transportation and Economics Research Institute The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited Beijing China Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Institute of Automation Chinese Academy of Sciences China University of Geosciences Beijing School of Information Engineering The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Rowan University Department of Electrical and Computer Engineering Glassboro United States

ISBN: (纸本)9798350337020

Global Navigation Satellite systems (GNSS) can provide real-time positioning information for outdoor users, but cannot for indoor scenarios or heavily occluded outdoor scenarios. Strap-down Inertial Navigation System (SINS) are widely used to locate people in complex interior or heavily occluded outdoor scenarios due to its light weight and low power consumption. However, IMU of SINS are noisy, and the sampling data error is large, which is a divergence of the error with time. Therefore, it will generate a positioning accumulation error, which affects the final positioning accuracy. The problem of cumulative IMU errors is usually dealt with by Zero-Velocity Update (ZUPT). The zero-velocity detection part of basic ZUPT method usually uses a single threshold to determine the gait of pedestrian, which often has the problem of gait misjudgment and omission. To address these problems, this paper proposes a composite conditional detection method to solve the problem of misjudgment in the zero-velocity interval. In addition, we redesign the zero-velocity update algorithm and uses the Cubature Kalman filter (CKF) for pedestrian positioning error correction. The experimental results demonstrate that the proposed ZUPT method based on dual-threshold detection can better detect the interval between pedestrian motion and stationery than ones with single threshold. The zero-velocity update algorithm based on CKF has higher performance than conventional EKF and UKF methods, which constrains the cumulative error of SINS to about 0.2% of the whole walking distance. © 2023 IEEE.

关键词： Cubature Kalman Filter Inertial Pedestrian Navigation Zero-Velocity Update

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Distributed Model Predictive Coordinated Control for Combined Heat and Power Load of an Integrated Energy System 11th

Distributed Model Predictive Coordinated Control for Combine...

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11th IFAC Symposium on Control of Power and Energy systems, CPES 2022

作者： Jin, Yuhui Wu, Xiao Shen, Jiong Lee, Kwang Y. Key Laboratory of Energy Thermal Conversion and Control Ministry of Education School of Energy and Environment Southeast University Nanjing210096 China Department of Electrical and Computer Engineering Baylor University One Bear Place 97356 WacoTX76798-7356 United States

Due to the strong coupling and multiple constraints of combined heat and power (CHP) generation in an integrated energy system (IES), the model predictive control (MPC) is widely adopted as the lower layer coordination controller. However, with the upgrade of capacity and the expansion of scale, centralized MPC (CMPC) is no longer capable of dealing with heavy-data, high dimensional IES optimization problem. To address this concern, distributed MPC (DMPC) is applied to coordinate the energy supply of CHP-IES. In view of the characteristics of CHP-IES, DMPC should be endowed with the capability of offset-free tracking and disturbance rejection. Hence, the disturbance augmentation method and the modification of objective function have been applied to facilitate each DMPC controller with the required abilities. Through simulation comparison with CMPC on the same theoretical model, the performance of modified cooperative DMPC (CDMPC) is on par with CMPC, while the computational burden is drastically relieved. © 2022 Elsevier B.V.. All rights reserved.

关键词： Disturbance rejection

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A Microwave Sensor for Detection of Heavy Metals in Traditional Chinese Medicine

A Microwave Sensor for Detection of Heavy Metals in Traditio...

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IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Jing Wu Hua-Feng Li Ming-Feng Xu Jin-Peng He Zhong-Qi Zhang Min Chen Rui-Sen Chen Guan-Long Huang Zi-Yu Pang Kam-Weng Tam School of Electronic and Information Engineering Foshan University Foshan Guangdong China Guangdong Provincial Key Laboratory of Industrial Intelligent Inspection Technology Foshan University Foshan China Department of Electrical and Computer Engineering University of Macau Macau SAR China

ISBN: (数字)9798350351019

ISBN: (纸本)9798350351026

This paper presents a microwave sensor for the detection of heavy metals in traditional Chinese medicine decoctions. The sensor is composed of interdigital capacitors (IDC), a split-ring resonators (SRR) coupled with a microstrip line on the substrate, and a test substance placed above the sensor. By altering the dielectric constant of the liquid, the sensor demonstrates a frequency shift, enabling the detection of heavy metal content. This design offers a high-sensitivity, non-invasive detection method suitable for real-time monitoring of heavy metals in traditional Chinese medicine decoctions.

关键词： Liquids Sensitivity Microstrip resonators Dielectric constant Metals Resonant frequency Microwave sensors Microstrip Resonators Substrates

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