检索结果-内蒙古大学图书馆

Distribution Locational Marginal Pricing Based Equilibrium Optimization Strategy for Data Center Park with Spatial-temporal Demand-side Resources

引用

Journal of Modern Power Systems and Clean Energy 2023年第6期11卷 1959-1970页

作者： Zhihao Yang Anupam Trivedi Ming Ni Haoming Liu Dipti Srinivasan IEEE College of Energy and Electrical Engineering Hohai UniversityNanjing 211100China Department of Electrical&Computer Engineering National University of SingaporeSingapore

This paper proposes a distribution locational marginal pricing(DLMP) based bi-level Stackelberg game framework between the internet service company(ISC) and distribution system operator(DSO) in the data center park. To minimize electricity costs, the ISC at the upper level dispatches the interactive workloads(IWs) across different data center buildings spatially and schedules the battery energy storage system temporally in response to DLMP. Photovoltaic generation and static var generation provide extra active and reactive power. At the lower level, DSO calculates the DLMP by minimizing the total electricity cost under the two-part tariff policy and ensures that the distribution network is uncongested and bus voltage is within the limit. The equilibrium solution is obtained by converting the bi-level optimization into a single-level mixed-integer second-order cone programming optimization using the strong duality theorem and the binary expansion method. Case studies verify that the proposed method benefits both the DSO and ISC while preserving the privacy of the ISC. By taking into account the uncertainties in IWs and photovoltaic generation, the flexibility of distribution networks is enhanced, which further facilitates the accommodation of more demand-side resources.

关键词： Bi-level optimization congestion management data center demand response distribution locational marginal pricing(DLMP) robust optimization

来源：评论

学校读者我要写书评

暂无评论

Robust Vector Current Control of Grid-Interactive Smart Packed E-Cell Multi-Level Inverters under Non-Ideal Grid Conditions

引用

IEEE Transactions on Energy Conversion 2024年第2期40卷 1009-1021页

作者： Sadabadi, Mahdieh S. Sharifzadeh, Mohammad Laurendeau, Eric Gheisarnejad, Meysam Khooban, Mohammad-Hassan Portillo, Ramon Franquelo, Leopoldo Garcia Al-Haddad, Kamal The University of Manchester Department of Electrical and Electronic Engineering Manchester United Kingdom Ecole de Technologie Superieure Department of Electrical Engineering MontrealQC Canada Aarhus University Department of Electrical and Computer Engineering Aarhus Denmark University of Seville Department of Electronic Engineering Seville Spain

This paper proposes a convex optimization-based strategy for the design of vector current controllers in a grid-interactive single-phase nine-level Packed E-Cell (PEC9) inverter under uncertain grid conditions, from stiff to weaker conditions, and non-ideal grid voltage conditions. The non-ideal grid-interactive PEC inverter with the grid impedance uncertainty is modeled by a multivariable polytopic model subject to a disturance in a state space framework. By virtue of this novel modeling approach, a robust two-degree-of-freedom H∞ multivariable Proportional-Integral (PI) vector current control strategy is then proposed. The proposed control mechanism guarantees robust stability, employing a quadratic Lyapunov function, and robust performance against the uncertainty in grid impedance and non-ideal grid voltage conditions, also it provides inverter currents with Total Harmonic Distortion and harmonic components complying with IEEE Standard 1547 requirements. The effectiveness of the proposed grid-interactive smart PEC9 inverter equipped with the proposed robust vector control strategy is evaluated by simulation and hardware-in-the-loop experimental case studies. © 1986-2012 IEEE.

关键词： Vectors

来源：评论

学校读者我要写书评

暂无评论

Printing Stretchable Electronics With Fused Filament Fabrication

IEEE Journal on Flexible Electronics

引用

IEEE Journal on Flexible Electronics 2025年第2期4卷 98-107页

作者： Jahan, Sharmin Lazarus, Nathan University of Delaware Electrical and Computer Engineering Department NewarkDE19716 United States

Additive manufacturing is a powerful approach forintegrating flexible and stretchable conductors into complex3-D structures, but many current printing technologies, suchas direct ink writing (DIW), are expensive and challengingto access for many potential users, such as hobbyists andsmall companies. In this work, a low-cost commercial fusedfilament fabrication (FFF) 3-D printer is used to manufacturelow-resistivity stretchable serpentine conductors. By using dualextruder heads, serpentines are printed in a highly conductivemetal-particulate-based composite on a thermoplastic elastomer(TPE) part and demonstrated to survive mechanical strains of upto 25%, sufficient for use in close contact with the human *** electroless plating is also demonstrated to metalize theprinted traces, depositing a thin layer of copper for improvedelectrical conductivity. Using this approach, we demonstrateconductivity improvements by about three orders of magnitude(∼ 103) over past FFF printed serpentines, a major milestonein making stretchable electronics cheaper and more *** resulting stretchable conductors are then demonstrated toprint devices, including stretchable inductors and stretchableinterconnect on a soft robotic actuator. © 2022 IEEE.

关键词： Reinforced plastics

来源：评论

学校读者我要写书评

暂无评论

Design and Utilization of Multiskewed Multibit Flip-Flop Cells for Timing Optimization: Design and Technology Co-Optimization Approach

引用

IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年第3期44卷 1084-1097页

作者： Kim, Suwan Kim, Taewhan Seoul National University Department of Electrical and Computer Engineering Seoul08826 Korea Republic of

Utilizing multibit flip-flops (MBFFs) in circuit implementation offers a considerable saving on the dynamic power dissipated at the clock networks. However, indiscreetly allocating MBFFs by grouping single-bit flip-flops at the logic synthesis or placement stage in order to greedily save dynamic power severely hinders a full applicability of useful clock skew scheduling to the individual flip-flops in MBFFs, failing in effectively optimizing circuit timing. This is because the two internal clock inverters, consequently, the clock skew value, in an MBFF are shared by all of the flip-flops in the MBFF. This work overcomes this inherent limitation of inflexibility in MBFFs for useful clock skew scheduling by proposing a comprehensive design and technology co-optimization (DTCO) framework. To this end, we devise a new layout of MBFF cells called multiskewed MBFF layout, in which different clock skew values can be set to the individual internal flip-flops at the cost of additional internal clock inverters. With the multiskewed MBFFs, we propose a three-step DTCO flow: 1) DTCO-based flip-flop clustering at the logic stage, which clusters flip-flops considering multiskewed MBFFs;2) DTCO based on in-place MBFF debanking technique at the preroute stage to facilitate the full applicability of useful skew scheduling at the subsequent stages;and 3) DTCO utilizing MBFF cell layout diversification at the post-route stage, by which useful clock skew scheduling can effectively resolve timing violations. Through experiments with OpenCores benchmark circuits, it is shown that our proposed DTCO flow of reinforcing the effectiveness of useful clock skew scheduling on circuits with MBFF instances is able to reduce the worst and total negative slacks by 36.73% and 50.76%, respectively, while decreasing the clock and total power consumption by 43.46% and 22.70% over that produced by the conventional flow using a state-of-the-art commercial tool. © 2024 IEEE.

关键词： Flip flop circuits

来源：评论

学校读者我要写书评

暂无评论

Ensemble of deep learning techniques to human activity recognition using smart phone signals

引用

Multimedia Tools and Applications 2024年第42期83卷 89635-89664页

作者： Imanzadeh, Soodabeh Tanha, Jafar Jalili, Mahdi Electrical and Computer Engineering Department University of Tabriz Tabriz Iran School of Engineering RMIT University Melbourne Australia

Human Activity Recognition (HAR) has become a significant area of study in the fields of health, human behavior analysis, the Internet of Things, and human–machine interaction in recent years. Smartphones are a popular choice for HAR as they are common devices used in daily life. However, most available HAR datasets are gathered in laboratory settings, which do not reflect real-world scenarios. To address this issue, a real-world dataset using smartphone inertial sensors, involving 62 individuals, is collected. The collected dataset is noisy, small, and has variable frequency. On the other hand, in the context of HAR, algorithms face additional challenges due to intra-class diversity (which refers to differences in the characteristics of performing an activity by different people or by the same individual under different conditions) and inter-class similarity (which refers to different activities that are highly similar). Consequently, it is essential to extract features accurately from the dataset. Ensemble learning, which combines multiple models, is an effective approach to improve generalization performance. In this paper, a weighted ensemble of hybrid deep models for HAR using smartphone sensors is proposed. The proposed ensemble approach demonstrates superior performance compared to current methods, achieving impressive results across multiple evaluation metrics. Specifically, the experimental analysis demonstrates an accuracy of 97.15%, precision of 96.41%, recall of 95.62%, and an F1-score of 96.01%. These results demonstrate the effectiveness of our ensemble approach in addressing the challenges of HAR in real-world scenarios. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Smartphones

来源：评论

学校读者我要写书评

暂无评论

Exploring Hyperdimensional Computing Robustness Against Hardware Errors

引用

IEEE Transactions on computers 2025年第6期74卷 1963-1977页

作者： Zhang, Sizhe Juretus, Kyle Jiao, Xun Villanova University Department of Electrical and Computer Engineering VillanovaPA19085 United States

Brain-inspired hyperdimensional computing (HDC) is an emerging machine learning paradigm leveraging high-dimensional spaces for efficient tasks like pattern recognition and medical diagnostics. As a lightweight alternative to deep neural networks, HDC offers smaller model sizes, reduced computation, and memory-centric processing. However, deploying HDC in safety-critical applications, such as healthcare and robotics, is challenged by hardware-induced errors. This paper investigates HDC's robustness to memory errors via extensive bit-flip injection experiments on item and associative memories. Results reveal that certain bit-flips severely degrade accuracy. To address this, we introduce the Hyperdimensional Bit-Flip Search (HD-BFS), a similarity-guided method for identifying vulnerabilities and crafting efficient attacks, where flipping just 6 critical bits—3.9% of random bit-flips—reduces accuracy to chance levels. We further propose Hyperdimensional Accelerated Bit-Flip Search (HD-ABFS), which narrows the search space by targeting critical dimensions and most significant bits (MSBs), achieving up to 282x speedup over HD-BFS. Finally, we develop an effective protection mechanism to enhance model safety. These insights highlight HDC's resilience to random errors, offer robust defenses against targeted attacks, and advance the security and reliability of HDC systems. © 1968-2012 IEEE.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Design and Modeling of a Photonic Crystal Multiplexer Using Artificial Intelligence

Advanced Electromagnetics

引用

Advanced Electromagnetics 2025年第1期14卷 59-64页

作者： Karami, Pouya Yahya, Salah I. Palash, Behnam Chaudhary, Muhammad Akmal Assaad, Maher Parandin, Fariborz Roshani, Saeed Hazzazi, Fawwaz Roshani, Sobhan Department of Electrical Engineering Kermanshah Branch Islamic Azad University Kermanshah Iran Department of Communication and Computer Engineering Cihan University-Erbil Erbil44001 Iraq Department of Software Engineering Faculty of Engineering Koya University KOY45 Koya Iraq Department of Electrical and Computer Engineering College of Engineering and Information Technology Ajman University 346 Ajman United Arab Emirates Department of Electrical Engineering College of Engineering Prince Sattam bin Abdulaziz University Al-Kharj11492 Saudi Arabia

In this paper, design and modeling of an all-optical 2×1 multiplexer based on 2D photonic crystals and artificial neural networks (ANNs) are presented. The proposed structure aims to maximize the difference between the output powers in logical states, which is critical for enhancing the system ability to distinguish between these states. In this study, an ANN model is employed to accurately predict the normalized output power of the designed photonic crystal multiplexer, providing a time-efficient alternative to conventional simulation methods for analyzing multiplexer behavior across various logical states. The results demonstrate significant improvements in signal separation and overall performance compared to previous works. Additionally, a detailed comparison of the normalized output power for different logic states is provided, highlighting the advantages of the proposed design. © 2025, Advanced Electromagnetics. All rights reserved.

关键词： Silicon photonics

来源：评论

学校读者我要写书评

暂无评论

Multi-Model emotion recognition from speech using StarGAN, DCNN, and SVM

引用

Multimedia Tools and Applications 2025年 1-18页

作者： Banihosseini, Najmeh Sadat Ghods, Vahid Department of Electrical and Computer Engineering Semnan Branch Islamic Azad University Semnan Iran

Emotion recognition from speech is a significant research area in human–computer interaction and psychological assessments. This study proposes a novel three-stage process for emotion recognition from speech signals. In the first stage, pre-processing operations, including noise reduction and normalization, are applied to improve data quality. Then, StarGAN is employed for data augmentation, addressing the challenge of limited emotional speech data. The second stage involves extracting meaningful features from high-dimensional data using deep convolutional neural networks (DCNN). Finally, support vector machines (SVM) are used to classify the extracted features into distinct emotional categories. The main advantage of the proposed method over traditional deep learning techniques is the use of StarGAN for data augmentation, which improves performance on limited datasets. Additionally, the combination of DCNN and SVM enhances accuracy and efficiency in feature extraction and classification. This method has been evaluated using four different datasets, RAVDESS, Emo-DB, SAVEE, and CASIA, achieving accuracy rates of 98.25%, 95.5%, 96.8%, and 96.45%, respectively. The results indicate that the proposed method significantly outperforms existing methods and offers improved capabilities for practical application. Despite achieving high accuracy, challenges such as increased computational complexity and variability in performance across different datasets are discussed. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Theoretical Analysis of Detection of Flying Vehicles Based on the Passive Radiometric Detection of Microwave-Millimeter-Terahertz Wavelength Electromagnetic Emissions from Exhaust Plasma Gases

引用

Progress in Electromagnetics Research C 2025年 154卷 267-275页

作者： Chatziathanasiou, Thomas N. Uzunoglu, Nikolaos Microwave Laboratory Department of Electrical and Computer Engineering National Technical University of Athens Greece

The feasibility of using passive radiometric detection of chaotic electromagnetic signals emanating from low density plasma plumes of the jet exhaust gases to detect low radar cross section aircrafts is analyzed for the frequency band 3.108–3.1012 Hz. The aircraft exhaust plume gas formation is modelled with a number of discrete different dimensions ellipsoids, with each having different electron densities and temperatures. Electromagnetic radiation analysis of emitting signals is computed applying fluctuation-dissipation theorem and geometric considerations. The detection range of characteristic military jet aircraft is computed numerically for the whole frequency band, from UHF to 3 THz. It is shown that high range detection can be achieved at upper microwave frequencies. © 2025, Electromagnetics Academy. All rights reserved.

关键词： Terahertz waves

来源：评论

学校读者我要写书评

暂无评论

Deep Reinforcement Learning Data Collection for Bayesian Inference of Hidden Markov Models

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1217-1232页

作者： Alali, Mohammad Imani, Mahdi Northeastern University Department of Electrical and Computer Engineering BostonMA02115 United States

Hidden Markov models (HMMs) are a powerful class of dynamical models for representing complex systems that are partially observed through sensory data. Existing data collection methods for HMMs, typically based on active learning or heuristic approaches, face challenges in terms of efficiency in stochastic domains with costly data. This article introduces a Bayesian lookahead data collection method for inferring HMMs with finite state and parameter spaces. The method optimizes data collection under uncertainty using a belief state that captures the joint distribution of system states and models. Unlike traditional approaches that prioritize short-term gains, this policy accounts for the long-term impact of data collection decisions to improve inference performance over time. We develop a deep reinforcement learning policy that approximates the optimal Bayesian solution by simulating system trajectories offline. This pretrained policy can be executed in real time, dynamically adapting to new conditions as data is collected. The proposed framework supports a wide range of inference objectives, including point based, distribution based, and causal inference. Experimental results across three distinct systems demonstrate significant improvements in inference accuracy and robustness, showcasing the effectiveness of the approach in uncertain and data-limited environments. © 2020 IEEE.

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：