检索结果-内蒙古大学图书馆

50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Chakkalakkal, Sreejith Sagar, Sadaraboina Moses Vidya Mirfakhrai, Tissaphern Liu, Wenpeng Bhavaraju, Vijay Dept. of Electrical & Computer Engineering McMaster University Hamilton Canada Center for Digital Engineering Eaton India Innovation Centre Pune India Qnovo Detroit United States Eaton Research Labs Southfield United States Eaton Research Labs GoldenCO United States

ISBN: (纸本)9781665464543

Building and facility-level battery energy storage systems have recently gained a great deal of attention due to the rapid revolution in battery technologies, a decreasing trend in the cost of battery storage systems, and increased demand for lowering building electricity bills. Building-integrated battery energy storage systems(BESS) balance the load power demand and power system connected capacity by storing the electricity for later use, increasing the flexibility and reliability of the utility network. This study compares the effect of battery management algorithms (BMS) on the customer return and life of Li-Ion battery energy storage systems used for building energy storage applications. Five battery management algorithms are proposed and compared for customer benefit and battery health. Finally, an optimal battery management algorithm is selected, which gives enhanced battery life and customer savings. This article concluded that proper sizing and control of the energy storage systems are instrumental in enhancing the battery life and customer return for a given application. © 2024 IEEE.

关键词： Sales

来源：评论

学校读者我要写书评

暂无评论

Matrix Resolvent Eigenembeddings for Dynamic Graphs 48

Matrix Resolvent Eigenembeddings for Dynamic Graphs

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Kalantzis, Vasileios Traganitis, Panagiotis A. Thomas J. Watson Research Center IBM Research Yorktown HeightsNY United States Michigan State University Dept. of Electrical and Computer Engineering East LansingMI United States

ISBN: (纸本)9781728163277

Eigenvector embeddings have been widely used to study graph properties in signal processing, mining, and learning tasks. However, if a graph is changing dynamically, these embeddings have to be recomputed. In this work we introduce a novel matrix resolvent expansion-based projection scheme to update eigenvector embeddings of dynamic graphs. The proposed method can tackle graph updates where both new vertices and edges are added, and its potential is illustrated via numerical tests on real data. © 2023 IEEE.

关键词： Eigenvalues and eigenfunctions

来源：评论

学校读者我要写书评

暂无评论

Towards Automated Nanoenergetic Reaction Characterization with Computational Vision

Towards Automated Nanoenergetic Reaction Characterization wi...

引用

2023 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2023

作者： Gaines, Timothy B. Hurt, J. Alex Keller, James M. Scott, Grant J. Boyle, Camden I. Maschmann, Matthew R. Price, Stanton R. University of Missouri Dept. of Electrical Engineering and Computer Science Columbia United States University of Missouri Dept. of Mechanical and Aerospace Engineering Columbia United States U.S. Army Engineer Research and Development Center VicksburgMS United States

ISBN: (纸本)9798350359527

In order to utilize aluminum-based nanoenergetic materials effectively, the mechanisms by which reactive aluminum fuel escapes a passivating aluminum shell must be better understood. These reactions can be classified based upon pre-and post-reaction imagery taken of the material. To aide in quantitatively understanding these reactions we investigate creating an improved reaction classifier through leveraging larger, state-of-The-Art deep learning models. Additionally, we experiment with various pre-Training methods and image analogs to enable effective training of large models even with a small dataset. Image features used from the past experimentation may be used in conjunction with reaction imagery to aid in reaction classification. © 2023 IEEE.

关键词： Aluminum

来源：评论

学校读者我要写书评

暂无评论

Visual Field Prediction for Fundus Image with Generative AI 18

Visual Field Prediction for Fundus Image with Generative AI

引用

18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Kang, Honggu Ko, Seonghyeon Kim, Juchan Le, Duc-Tai Bum, Junghyun Han, Jongchul Choo, Hyunseung Dept. of AI Systems Engineering Sungkyunkwan University Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Sungkyun AI Research Institute Sungkyunkwan University Suwon Korea Republic of Dept. of Ophthalmology Samsung Medical Center Seoul Korea Republic of Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798350331011

Accurate diagnosis of glaucoma is crucial due to its high risk of blindness, but the long examination time often undermines the reliability of results by examinee's subjective factors. We propose a method to reduce examination time by generating static perimetry results with Conventional Fundus Images (CFIs) utilizing the CFI2GM technique, which leverages multimodal data. Based on data from 3,306 glaucoma patients at Samsung Medical center in Seoul, we conducted ophthalmic image translation utilizing the Pix2Pix model. Our method, combining cGAN, L1, and SSIM loss, achieved MSE 57.9886 and PSNR 30.6057 dB. Furthermore, we received positive feedback from ophthalmologist regarding the high practical applicability of the images generated by our method. This indicates that CFI2GM can enhance the reliability of glaucoma examination results as well as reduce testing time. © 2024 IEEE.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

A Rate-Distortion Framework for Summarization

arXiv

引用

arXiv 2025年

作者： Arda, Enes Yener, Aylin INSPIRE@OhioState Research Center Dept. of Electrical and Computer Engineering The Ohio State University United States

THIS PAPER IS ELIGIBLE FOR THE STUDENT PAPER AWARD. This paper introduces an information-theoretic framework for text summarization. We define the summarizer rate-distortion function and show that it provides a fundamental lower bound on summarizer performance. We describe an iterative procedure, similar to Blahut-Arimoto algorithm, for computing this function. To handle real-world text datasets, we also propose a practical method that can calculate the summarizer rate-distortion function with limited data. Finally, we empirically confirm our theoretical results by comparing the summarizer rate-distortion function with the performances of different summarizers used in practice. Copyright © 2025, The Authors. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Task Reverse Offloading with Deep Reinforcement Learning in Multi-Access Edge Computing 9

Task Reverse Offloading with Deep Reinforcement Learning in ...

引用

9th International Conference on computer and Communication engineering, ICCCE 2023

作者： Saeed, Mamoon M. Saeed, Rashid A. Mokhtar, Rania A. Khalifa, Othman O. Ahmed, Zeinab E. Barakat, Mohammed Elnaim, Areeg Ali Faculty of Engineering Department of Communications and Electronics Engineering Yemen Taif University College of Computers and Information Technology Department of Computer Engineering P.O. Box 11099 Taif21944 Saudi Arabia International Islamic University Malaysia Department of Electrical and Computer Engineering Malaysia Libyan Center for Engineering Research and Information Technology Bani Waleed Libya International Islamic University Malaysia Electrical and Computer Engineering Dept. Malaysia University of Gezira Computer Engineering Dept. Sudan Alneelain University College of Engineering Khartoum Sudan

ISBN: (纸本)9798350325218

The Multi-access Edge Computing (MEC) technology's quick development greatly benefits the Collaborative Mobile Infrastructure System (CMIS). To combine the data and produce tasks, crowd-sensing data will be transferred to the MEC server in CMIS. Nevertheless, if there are too many devices, it becomes extremely difficult for MEC to decide appropriately based on the data from the devices and infrastructure. This study builds a framework for reverse offloading that carefully balances the relationship between task completion time and user mobile energy consumption. Moreover, to decrease system use generally, an adaptive optimal reverse offloading method based on Deep Q-Network is created (DQN). The results of the simulations demonstrate that the suggested approach may successfully minimize energy consumption and work latency when compared to full local and fixed offloading techniques. © 2023 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Pursuit-Evasion on a Sphere and When It Can Be Considered Flat 63

Pursuit-Evasion on a Sphere and When It Can Be Considered Fl...

引用

63rd IEEE Conference on Decision and Control, CDC 2024

作者： Milutinovic, Dejan Moll, Alexander Von Manyam, Satyanarayana G. Casbeer, David W. Weintraub, Isaac E. Pachter, Meir Electrical and Computer Engineering Dept. University of California Santa Cruz Santa CruzCA United States Control Science Center Aerospace Systems Directorate Air Force Research Laboratory DaytonOH45435 United States Dcs Corporation DaytonOH United States Air Force Institute of Technology Wright-Patterson Afb Dept. of Electrical Engineering OH United States

ISBN: (纸本)9798350316339

In planar pursuit-evasion differential games considering a faster pursuer and slower evader, the interception points resulting from equilibrium strategies lie on the Apollonius circle. This property is instrumental for leveraging geometric approaches for solving multiple pursuit-evasion scenarios in the plane. Here, we study a pursuit-evasion differential game on a sphere and generalize the planar Apollonius set to the spherical domain. We find that the interception point from the equilibrium strategies can leave the Apollonius set boundary and present a condition to keep the intercept point on the boundary. This condition allows for generalizing planar pursuitevasion strategies to the sphere. © 2024 IEEE.

关键词： Spheres

来源：评论

学校读者我要写书评

暂无评论

Ensuring Model Fairness via Stratified Training: TP53 Mutation Prediction with Estrogen Receptor Stratification in Breast Histopathology 46

Ensuring Model Fairness via Stratified Training: TP53 Mutati...

引用

46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Tsiknakis, Nikos Wang, Kang Salgkamis, Dimitrios Tzoras, Evangelos Manikis, Georgios C. Sifakis, Emmanouil Bergh, Jonas Zerdes, Ioannis Marias, Kostas Matikas, Alexios Foukakis, Theodoros Karolinska Institutet Stockholm17177 Sweden Breast Cancer Center Karolinska University Hospital Stockholm17177 Sweden Institute of Computer Science Foundation for Research and Technology Hellas Heraklion70013 Greece Hellenic Mediterranean University Dept. of Electrical and Computer Engineering Heraklion71410 Greece

ISBN: (纸本)9798350371499

Developing AI models on medical images as decision support systems has seen a huge increase in interest during the last few years. However, most published studies have neglected testing the model's robustness against certain dataset-related biases and unbalanced variables. For example, although the prevalence of TP53 mutations is higher in Estrogen Receptor (ER)-negative breast cancer, while most ER-positive tumors are not mutated, published models have been developed on the entirety of the available data without testing for such intrinsic biases that can lead to overfitting. In this study we show that models trained for TP53 mutation prediction overfit on ER status and that stratification of training on the basis of ER is beneficial for all subgroups while it reduces bias and increases generalizability and fairness. (Implementation: https://***/tsikup/er-stratified-training-tp53-prediction) © 2024 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Semantic Forwarding for Next Generation Relay Networks

arXiv

引用

arXiv 2024年

作者： Arda, Enes Kutay, Emrecan Yener, Aylin INSPIRE@OhioState Research Center Dept. of Electrical and Computer Engineering The Ohio State University United States

We consider cooperative semantic text communications facilitated by a relay node. We propose two types of semantic forwarding: semantic lossy forwarding (SLF) and semantic predict-and-forward (SPF). Both are machine learning aided approaches, and, in particular, utilize attention mechanisms at the relay to establish a dynamic semantic state, updated upon receiving a new source signal. In the SLF model, the semantic state is used to decode the received source signal;whereas in the SPF model, it is used to predict the next source signal, enabling proactive forwarding. Our proposed forwarding schemes do not need any channel state information and exhibit consistent performance regardless of the relay’s position. Our results demonstrate that the proposed semantic forwarding techniques outperform conventional semantic-agnostic baselines. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

EADTC: An Approach to Interpretable and Accurate Crime Prediction

EADTC: An Approach to Interpretable and Accurate Crime Predi...

引用

2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Ma, Yujunrong Nakamura, Kiminori Lee, Eung-Joo Bhattacharyya, Shuvra S. University of Maryland Dept. of Electrical and Computer Engineering United States University of Maryland Dept. of Criminology and Criminal Justice United States Mgh and Harvard Medical School Camca Dept. of Radiology United States Institute for Advanced Computer Studies Maryland Crime Research and Innovation Center University of Maryland Dept. of Electrical and Computer Engineering United States

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Machine learning applications related to high-stakes decisions are often surrounded by significant amounts of controversy. This has led to increasing interest in interpretable machine learning models. A well-known class of interpretable models is that of decision trees (DTs), which mirror a common strategy used by humans to arrive at solutions through a series of well-defined decisions. However, much of previous research on DTs for criminal justice predictions has focused primarily on collections (ensembles) of DTs whose results are aggregated together. Such DT ensembles are used to help improve accuracy;however, their increased complexity and deviation from human decision-making processes makes them much less interpretable compared to single-DT approaches. In this paper, we present a new DT model for criminal recidivism prediction that is designed with high interpretability, accuracy, and fairness as core objectives. The interpretability of the model stems from its formulation in terms of a single DT structure, while accuracy is achieved through an intensive optimization process of DT parameters that is carried out using a novel evolutionary algorithm. Through extensive experiments, we analyze the performance of our proposed EADTC (Evolutionary Algorithm Decision Tree for Crime prediction) method on relevant datasets. Our experiments show that the EADTC approach achieves competitive accuracy and fairness with respect to state-of-the-art ensemble DT models, while achieving higher interpretability due to the simpler, single-DT structure. © 2022 IEEE.

关键词： Decision trees

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：