检索结果-内蒙古大学图书馆

2024 Resilience Week, RWS 2024

作者： Boamah, Sharon A. Mathieu, Reynold Faillace, Victor Agnew, Dennis McNair, Janise Bretas, Arturo University of Florida Department of Electrical and Computer Engineering GainesvilleFL United States Sandia National Laboratory Distributed Systems Group United States

ISBN: (纸本)9798350388985

The integration of information communication technology with the power grid exposes it to cyber threats. The network state estimation process provides stability to the smart grid. The communication network plays a major role in ensuring the successful transmission of state information. However, these network measurements are vulnerable to malicious attacks. This subsequently affects the network measurement such as associated high transmission delays and packet losses affecting the reliability of the smart grid. In this work, we propose a hybrid physics-based data-driven model that uses data fusion from the state-of-the-art physics-based Network State Estimation model and a data-driven model to detect false data injection attacks in the communication network layer of the smart grid. The performance of the data fusion method is evaluated and the simulation results show that the proposed model outperforms the standalone approaches in the detection of bad data. This shows that the proposed scheme is able to improve the cyber-physical security of the communication network layer of the smart grid. © 2024 IEEE.

关键词： Cyber attacks

来源：评论

学校读者我要写书评

暂无评论

Automatic extraction of lightweight and efficient neural network architecture of heavy convolutional architectures to predict microsatellite instability from hematoxylin and eosin histology in gastric cancer

引用

Neural Computing and Applications 2024年第25期36卷 15295-15321页

作者： Rostami, Habib Ashkpour, Maryam Behzadi-Khormouji, Hamed Mokhtari, Maral Khayati, Armin Keshavarz, Ahmad Talatian Azad, Saeed Tabesh, Yahya Computer Engineering Department Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75168 Iran Artificial Intelligence and Intelligent Healthcare Lab Artificial Intelligence and Data Mining Research Group Faculty of Intelligent Systems Engineering and Data Science ICT Research Institute Persian Gulf University Bushehr75168 Iran Faculty of New Science and Technologies University of Tehran Tehran Iran Department of Computer Science Imec-IDLab University of Antwerp Sint-Pietersvliet 7 Antwerp2000 Belgium Shahid Faghihi Hospital Shiraz University of Medical Sciences Shiraz Iran Department of Computer Science Engineering and IT School of Electrical and Computer Engineering Shiraz University Shiraz Iran Electrical Engineering Department Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75168 Iran IoT and Signal Processing Research Group Faculty of Intelligent Systems Engineering and Data Science ICT Research Institute Persian Gulf University Bushehr75168 Iran Department of Mathematical Sciences Sharif University of Technology Tehran Iran

Cancers have emerged as a significant concern due to their impact on public health and society. The examination and interpretation of tissue sections stained with Hematoxylin and Eosin (H&E) play a crucial role in disease assessment, particularly in cases like gastric cancer. Microsatellite instability (MSI) is suggested to contribute to the carcinogenesis of specific gastrointestinal tumors. However, due to the nonspecific morphology observed in H&E-stained tissue sections, MSI determination often requires costly evaluations through various molecular studies and immunohistochemistry methods in specialized molecular pathology laboratories. Despite the high cost, international guidelines recommend MSI testing for gastrointestinal cancers. Thus, there is a pressing need for a new diagnostic modality with lower costs and widespread applicability for MSI detection. This study aims to detect MSI directly from H&E histology slides in gastric cancer, providing a cost-effective alternative. The performance of well-known deep convolutional neural networks (DCNNs) and a proposed architecture are compared. Medical image datasets are typically smaller than benchmark datasets like ImageNet, necessitating the use of off-the-shelf DCNN architectures developed for large datasets through techniques such as transfer learning. Designing an architecture proportional to a custom dataset can be tedious and may not yield desirable results. In this work, we propose an automatic method to extract a lightweight and efficient architecture from a given heavy architecture (e.g., well-known off-the-shelf DCNNs) proportional to a specific dataset. To predict MSI instability, we extracted the MicroNet architecture from the Xception network using the proposed method and compared its performance with other well-known architectures. The models were trained using tiles extracted from whole-slide images, and two evaluation strategies, tile-based and whole-slide image (WSI)-based, were employed and comp

关键词： Network architecture

来源：评论

学校读者我要写书评

暂无评论

Guidance of Agricultural Ground Robots Team with an Aerial Vehicle: A Cost-Effective Solution 56

Guidance of Agricultural Ground Robots Team with an Aerial V...

引用

56th International Symposium on Robotics, ISR Europe 2023

作者： Ugurlu, Halil Ibrahim Bardakci, Deniz Pham, Huy Xuan Kayacan, Erdal The Department of Electrical and Computer Engineering Aarhus University Aarhus C8000 Denmark Automatic Control Group Department of Electrical Engineering and Information Technology Paderborn University Paderborn Germany

ISBN: (纸本)9783800761418

Increasing the operational efficiency of agricultural machines is essential by the use of artificial intelligence (AI)-based navigation, planning, and control algorithms to handle the increasing demand for food production without compromising sustainability. In this study, an end-to-end path planning algorithm (AgroRL) is proposed for aerial-ground robots team collaboration. In the proposed solution, while main operations in the field are handled by the ground vehicle, the aerial robot is responsible for re-planning a collision-free trajectory for the ground robot when the robot faces an obstacle. Deep reinforcement learning is used for training the end-to-end policy for local re-planning of the aerial robot. The agent, informed by the global trajectory, generates local plans based on depth images. Variational autoencoders are also investigated for dimension reduction of the depth images in obstacle avoidance context to speed up deep reinforcement learning and alleviate the computational complexity of the policy network. The agriculture environment is developed in the Webots open-source robot simulator for training and testing purposes. The efficiency and efficacy of the end-to-end planner are evaluated over a number of cluttered field scenarios. The simulation experiments demonstrate a single aerial vehicle guiding multiple ground robots in agricultural operations. © VDE VERLAG GMBH Berlin Offenbach.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Enhancing Septic Shock Detection through Interpretable Machine Learning

引用

computer Modeling in engineering & Sciences 2024年第12期141卷 2501-2525页

作者： Md Mahfuzur Rahman Md Solaiman Chowdhury Mohammad Shorfuzzaman Lutful Karim Md Shafiullah Farag Azzedin Department of Information and Computer Science King Fahd University of Petroleum and MineralsDhahran31261Saudi Arabia Department of Electrical and Computer Engineering North South UniversityDhaka1229Bangladesh Department of Computer Science Taif UniversityTaif21974Saudi Arabia College of Applied Arts and Technology Seneca PolytechnicTorontoM2J 2X5Canada Department of Control&Instrumentation Engineering King Fahd University of Petroleum and MineralsDhahran31261Saudi Arabia

This article presents an innovative approach that leverages interpretable machine learning models and cloud computing to accelerate the detection of septic shock by analyzing electronic health *** traditional methods,which often lack transparency in decision-making,our approach focuses on early detection,offering a proactive strategy to mitigate the risks of *** integrating advanced machine learning algorithms with interpretability techniques,our method not only provides accurate predictions but also offers clear insights into the factors influencing the model’s ***,we introduce a preference-based matching algorithm to evaluate disease severity,enabling timely interventions guided by the analysis *** innovative integration significantly enhances the effectiveness of our *** leverage a clinical health dataset comprising 1,552,210 Electronic Health Records(EHR)to train our interpretable machine learning models within a cloud computing *** techniques like feature importance analysis and model-agnostic interpretability tools,we aim to clarify the crucial indicators contributing to septic shock *** transparency not only assists healthcare professionals in comprehending the model’s predictions but also facilitates the integration of our system into existing clinical *** validate the effectiveness of our interpretable models using the same dataset,achieving an impressive accuracy rate exceeding 98%through the application of oversampling *** findings of this study hold significant implications for the advancement of more effective and transparent diagnostic tools in the critical domain of sepsis management.

关键词： Sepsis prediction machine learning cloud computing

来源：评论

学校读者我要写书评

暂无评论

Conditioned fully convolutional denoising autoencoder for multi-target NILM

引用

Neural Computing and Applications 2024年 1-15页

作者： García, Diego Pérez, Daniel Papapetrou, Panagiotis Díaz, Ignacio Cuadrado, Abel A. Enguita, José M. Domínguez, Manuel Department of Electrical Engineering University of Oviedo Principado de Asturias Gijón33209 Spain SUPPRESS Research Group University of León León24007 Spain Department of Computer and Systems Science Stockholm University Kista SE-164 07 Stockholm Sweden

Energy management requires reliable tools to support decisions aimed at optimising consumption. Advances in data-driven models provide techniques like Non-Intrusive Load Monitoring (NILM), which estimates the energy demand of appliances from total consumption. Common single-target NILM approaches perform energy disaggregation by using separate learned models for each device. However, the use of single-target systems in real scenarios is computationally expensive and can obscure the interpretation of the resulting feedback. This study assesses a conditioned deep neural network built upon a Fully Convolutional Denoising AutoEncoder (FCNdAE) as multi-target NILM model. The network performs multiple disaggregations using a conditioning input that allows the specification of the target appliance. Experiments compare this approach with several single-target and multi-target models using public residential data from households and non-residential data from a hospital facility. Results show that the multi-target FCNdAE model enhances the disaggregation accuracy compared to previous models, particularly in non-residential data, and improves computational efficiency by reducing the number of trainable weights below 2 million and inference time below 0.25 s for several sequence lengths. Furthermore, the conditioning input helps the user to interpret the model and gain insight into its internal behaviour when predicting the energy demand of different appliances. © The Author(s) 2024.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Out-of-distribution Object Detection through Bayesian Uncertainty Estimation 26

Out-of-distribution Object Detection through Bayesian Uncert...

引用

26th International Conference on Information Fusion, FUSION 2023

作者： Zhang, Tianhao Wang, Shenglin Bouaynaya, Nidhal Calinescu, Radu Mihaylova, Lyudmila The University of Sheffield Automatic Control and Systems Engineering United Kingdom Rowan University Department of Electrical and Computer Engineering United States University of York Department of Computer Science United Kingdom

ISBN: (纸本)9798890344854

The superior performance of object detectors is often established under the condition that the test samples are in the same distribution as the training data. However, in many practical applications, out-of-distribution (OOD) instances are inevitable and usually lead to uncertainty in the results. In this paper, we propose a novel, intuitive, and scalable probabilistic object detection method for OOD detection. Unlike other uncertainty-modeling methods that either require huge computational costs to infer the weight distributions or rely on model training through synthetic outlier data, our method is able to distinguish between in-distribution (ID) data and OOD data via weight parameter sampling from proposed Gaussian distributions based on pre-trained networks. We demonstrate that our Bayesian object detector can achieve satisfactory OOD identification performance by reducing the FPR95 score by up to 8.19% and increasing the AUROC score by up to 13.94% when trained on BDD100k and VOC datasets as the ID datasets and evaluated on COCO2017 dataset as the OOD dataset. © 2023 International Society of Information Fusion.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Security Flaws From Time-Varying Encoding in High Speed MDI QKD

Security Flaws From Time-Varying Encoding in High Speed MDI ...

引用

2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Gnanapandithan, Amita Lo, Hoi-Kwong Qian, Li Department of Electrical and Computer Engineering University of Toronto Toronto Canada Department of Physics University of Toronto Canada Center for Quantum Information and Quantum Control University of Toronto Canada Department of Physics University of Hong Kong Hong Kong

ISBN: (纸本)9781957171258

Electro-optic phase modulators are commonly used for polarization and phase encoding in quantum key distribution. Here, a novel state preparation flaw which arises during high speed electro-optic phase modulation is identified and characterized. The impact of this state preparation flaw on the secure key rate is quantified. © Optica Publishing group 2023 © 2023 The Author(s)

关键词： Phase modulation

来源：评论

学校读者我要写书评

暂无评论

Low-Complexity Hardware Architecture of an H.264-Based Video Encoder for FPGAs

Low-Complexity Hardware Architecture of an H.264-Based Video...

引用

2023 Congress in computer Science, computer engineering, and Applied Computing, CSCE 2023

作者： Tayyebi, Azam Hanna, Darrin Jones, Bryant Oakland University Department of Electrical and Computer Engineering Rochester United States Intrepid Control Systems Troy United States

ISBN: (纸本)9798350327595

In this paper, we present an architecture for a scalable, efficient, realtime intra H.264 video encoder implemented on an FPGA. Our architecture was designed to achieve a through-put of up to 2.3 Gbit/s using a parallel and pipelined architecture described in VHDL. All modules in the architecture are optimized to utilize minimum hardware area. A parameterized encoding system and flexible architecture is proposed to provide the ability to achieve different compression ratios ranging from 1.4 to 2 with varying size and power requirements. As a baseline, with no compression, the encoder required hardware resources equivalent to 18K logic gates. This work experimented with compression ratios up to 2 which required an equivalent of 31K logic gates. The encoder performs at frequency ranges of 159-183 MHz. © 2023 IEEE.

关键词： CAVLC H.264 integer-transform intra-prediction quantization

来源：评论

学校读者我要写书评

暂无评论

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration 31

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration

引用

31st IEEE International Symposium on High Performance computer Architecture, HPCA 2025

作者： Chen, Yuzong Abouelhamayed, Ahmed F. Dai, Xilai Wang, Yang Andronic, Marta Constantinides, George A. Abdelfattah, Mohamed S. Cornell University Computer Systems Lab United States Systems and Networking Research Group Microsoft Research United States Imperial College London Department of Electrical and Electronic Engineering United Kingdom

ISBN: (纸本)9798331506476

Large language models (LLMs) have demonstrated remarkable performance across various machine learning tasks. Yet the substantial memory footprint of LLMs significantly hinders their deployment. In this paper, we improve the accessibility of LLMs through BitMoD1, an algorithm-hardware co-design solution that enables efficient LLM acceleration at low weight precision. On the algorithm side, BitMoD introduces fine-grained data type adaptation that uses a different numerical data type to quantize a group of (e.g., 128) weights. Through the careful design of these new data types, BitMoD is able to quantize LLM weights to very low precision (e.g., 4 bits and 3 bits) while maintaining high accuracy. On the hardware side, BitMoD employs a bitserial processing element to easily support multiple numerical precisions and data types;our hardware design includes two key innovations: First, it employs a unified representation to process different weight data types, thus reducing the hardware cost. Second, it adopts a bit-serial dequantization unit to rescale the per-group partial sum with minimal hardware overhead. Our evaluation on six representative LLMs demonstrates that BitMoD significantly outperforms state-of-the-art LLM quantization and acceleration methods. For discriminative tasks, BitMoD can quantize LLM weights to 4 -bit with © 2025 IEEE.

关键词： Integrated circuit design

来源：评论

学校读者我要写书评

暂无评论

Adaptive Fault Detection Scheme Using an Optimized Self-healing Ensemble Machine Learning Algorithm

引用

CSEE Journal of Power and Energy systems 2022年第4期8卷 1145-1156页

作者： Levent Yavuz Ahmet Soran AhmetÖnen Xiangjun Li S.M.Muyeen Electrical and Computer Engineering Department in Abdullah Gül University Kayseri 38080Turkey Department of Electrical and Computer Engineering College of EngineeringSultan Qaboos UniversityAl-KhoudMuscat123Oman Department of Electrical Engineering Qatar UniversityDoha 2713Qatar State Key Laboratory of Control and Operation of Renewable Energy and Storage Systems China Electric Power Rescarch InstituteBeijing 100192China

This paper proposes a new cost-efficient,adaptive,and self-healing algorithm in real time that detects faults in a short period with high accuracy,even in the situations when it is difficult to *** than using traditional machine learning(ML)algorithms or hybrid signal processing techniques,a new framework based on an optimization enabled weighted ensemble method is developed that combines essential ML *** the proposed method,the system will select and compound appropriate ML algorithms based on Particle Swarm Optimization(PSO)*** this purpose,power system failures are simulated by using the PSCA D-Python *** of the salient features of this study is that the proposed solution works on real-time raw data without using any pre-computational techniques or pre-stored ***,the proposed technique will be able to work on different systems,topologies,or data *** proposed fault detection technique is validated by using PSCAD-Python co-simulation on a modified and standard IEEE-14 and standard IEEE-39 bus considering network faults which are difficult to detect.

关键词： Decision tree(DT) ensemble machine learning algorithm fault detection islanding operation k-Nearest Neighbor(kNN) linear discriminant analysis(LDA) logistic regression(LR) Naive Bayes(NB) self-healing algorithm

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：