检索结果-内蒙古大学图书馆

6th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Kayikci, Safak Khoshgoftaar, Taghi Department of Electrical Engineering and Computer Science Florida Atlantic University Boca Raton United States

ISBN: (纸本)9798350394634

The main method for screening for breast cancer is mammography, which aims to lower the risk of breast cancer death by early detection. Deep learning techniques have been found to show good predictive results with many medical image datasets. Even so, extensive data augmentation approaches are necessary for accurate computer-assisted diagnosis due to the scarcity of annotated medical images. After being trained on photos, Generative Adversarial Networks (GANs) can produce new images that resemble real images and have a lot of genuine features. The realism and representativeness of the generated features can vary depending on the factors like quality of training data, training parameters, evaluation metrics, and application domain. We used a Histogram-Based Gradient Booster classifier to improve the traditional deep learning methods and encourage the use of GANs for data augmentation. When compared to the baseline, the results demonstrate that GANs increased recall without lowering precision and enhanced classification performance on the malignant class without sacrificing performance on the benign class. © 2024 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Training Photonic Mach Zehnder Meshes for Neural Network Acceleration 31

Training Photonic Mach Zehnder Meshes for Neural Network Acc...

引用

31st Annual IEEE International Conference on High Performance Computing, Data, and Analytics, HiPC 2024

作者： Wolff, Andy Karanth, Avinash School of Electrical Engineering and Computer Science Ohio University AthensOH45701 United States

ISBN: (纸本)9798331509095

Photonic neural networks enable faster and energy-efficient inferences for deep neural network implementations when compared to electrical counterparts. Prior training methods for photonic neural networks modify the network or decompose the inputs or weights to simplify the training, however that could impact accuracy. electrical training uses backpropagation that needs to partially differentiate each of the weights with respect to the loss function on a number of examples to achieve high accuracy. While the traditional backpropagation algorithm can be applied to electrical networks, it is difficult to directly apply to photonic neural network with Mach-Zehnder Interferometer (MZI) meshes that have different parameters (frequency and phase). In this paper, we implement the traditional backpropagation algorithm to train the parameters of the MZI meshes with gradient descent. Parameters of the mesh are trained by implementing a modified backpropagation algorithm on the hardware, and those operations may be performed on device. The implementation does not modify the circuits or add any additional lasers and therefore, self-trains the MZI to achieve the desired accuracy and achieve O(n2) speedup over electrical methods. We test our training algorithms on MNIST datasets and show that our method improves the training when compared to state-of-the-art electrical backpropagation. © 2024 IEEE.

关键词： backpropagation hardware accelerator photonic neural networks

来源：评论

学校读者我要写书评

暂无评论

Comparative analysis of machine learning and ensemble approaches for hepatitis B prediction using data mining with synthetic minority oversampling technique

引用

Health and Technology 2024年第1期14卷 109-118页

作者： Alizargar, Azadeh Chang, Yang-Lang Tan, Tan-Hsu Liu, Tsung-Yu Department of Electrical Engineering College of Electrical Engineering and Computer Science National Taipei University of Technology Taipei 10608 Taiwan Department of Multimedia and Game Science Lunghwa University of Science and Technology Taoyuan 333326 Taiwan

Purpose: Hepatitis B, caused by the Hepatitis B virus (HBV), can harm the liver without noticeable symptoms. Early detection is crucial to prevent transmission and enhance recovery. The main goal is to predict Hepatitis B through cost-effective lab test data, by utilizing machine learning. The primary focus is on evaluating the effectiveness of various algorithms in predicting the disease and their potential to enhance early diagnosis capabilities. Methods: Six distinct algorithms (Support Vector Machine, K-nearest Neighbors, Logistic Regression, decision tree, extreme gradient boosting, random forest) were employed alongside an ensemble model. Analysis involved two rounds: considering all features and key attributes. The Synthetic Minority Oversampling Technique (SMOTE) was employed for data imbalance. Various metrics, including the confusion matrix, precision, recall, F1 score, accuracy, receiver operating characteristics (ROC) curve, area under the curve (AUC), and mean absolute error (MAE), were utilized to assess the efficacy of each predictive technique. The National Health and Nutrition Examination Survey (NHANES) dataset was employed. Results: The experimental results demonstrate that the ensemble model attained the highest accuracy (97%) and AUC (0.997) in comparison to existing models. The analysis revealed that specific crucial features possess substantial predictive significance within this model. Conclusion: The study underscores the potential of the ensemble model as a valuable tool for medical practitioners, leveraging cost-effective and readily obtainable laboratory test data to predict Hepatitis B with remarkable accuracy. By facilitating early diagnosis and intervention, this research presents a promising avenue to enhance patient outcomes in the context of Hepatitis B. © 2023, The Author(s) under exclusive licence to International Union for Physical and engineering sciences in Medicine (IUPESM).

关键词： Early detection Ensemble model Index terms— Hepatitis B Liver damage Machine learning SMOTE

来源：评论

学校读者我要写书评

暂无评论

Fast Distributed Memory Parallel Algorithms for Finding Connected Components in Large Graphs

Fast Distributed Memory Parallel Algorithms for Finding Conn...

引用

2024 IEEE International Conference on Big Data, BigData 2024

作者： Khan, Maleq Boddu, Sharon Texas A&m University-Kingsville Department of Electrical Engineering and Computer Science United States

ISBN: (纸本)9798350362480

Finding the connected components in a graph is a fundamental problem in graph theory and network science. A connected component in a graph is a maximal set of vertices such that there is a path between any two vertices in the component. In a serial setting, connected components can be efficiently computed by running a breadth-first search (BFS). However, BFS-based algorithms do not lead to good parallelization. At the early stage, parallel algorithms for the connected component problem were developed for the PRAM model and shared-memory systems. With the availability of big data and big graphs and the development of a new area called network science, there is a renewed interest in developing parallel algorithms for this problem. In recent years, a number of distributed-memory parallel algorithms have been developed. These algorithms are based on mainly Shiloach and Vishkin's PRAM algorithm, called the SV algorithm, or some variants of SV. In this paper, we present a PRAM algorithm, called SELP, which is elegant and significantly simpler than the SV algorithm. Based on SELP, we develop a distributed-memory parallel algorithm, called DSELP. In addition, we incorporate some novel techniques to minimize communications among the processors. Experimental results show that our algorithms outperform the recent state-of-the-art distributed-memory algorithms significantly on a wide variety of graphs, and scale very well to a large number of processors. © 2024 IEEE.

关键词： Consensus algorithm

来源：评论

学校读者我要写书评

暂无评论

Real Time Modeling and Control Algorithm of a Grid-Connected Battery Energy Storage System

Real Time Modeling and Control Algorithm of a Grid-Connected...

引用

2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Karimi, Sepehr Nazaralizadeh, Solmaz Srivastava, Anurag Salem, Aboubakr Famouri, Parviz West Virginia University Lane Department of Computer Science and Electrical Engineering Morgantown United States WVU-IT Electrical and Computer Engineering Dept. Beckley United States PCS Eaton PittsburghPA United States

ISBN: (纸本)9798331541125

This work develops real time model and associate control for a grid-tied battery energy storage system (BESS), based on the real industrial system specifications, 362kW/1499kWh lithium-ion BESS. An active and reactive power controller based on the dq reference frame is designed for the grid-tied inverter. To ensure system performance, an LCL filter with passive damping is also developed and analyzed. The system is tested using the Controller Hardware-in-the-Loop (CHIL) platform: Typhoon. The results demonstrate the controller's effective reference tracking across varying load conditions, with the filter design achieving a 3.3% output current THD, emphasizing the inverter's optimized performance. © 2025 IEEE.

关键词： Battery storage

来源：评论

学校读者我要写书评

暂无评论

The development of assisted- visually impaired people robot in the indoor environment based on deep learning

引用

Multimedia Tools and Applications 2024年第3期83卷 6555-6578页

作者： Hsieh, Yi-Zeng Ku, Xiang-Long Lin, Shih-Syun Department of Electrical Engineering National Taiwan University of Science and Technology Taipei City106 Taiwan Department of Computer Science and Engineering National Taiwan Ocean University Keelung City Taiwan

The indoor positioning for visually impaired people has influence on their daily life in unknown indoor environment. This study designs the robot that can assist the blind walking safety and navigate in indoor environment by a single camera. The sense classification is proposed to position the blind in indoor environment by proposed convolutional neural network framework and integrate the semantic segmentation to find the road surface through a depth camera to guide the blind walking. The proposed vision-based sense classification method is compared with the traditional WiFi triangular-positioning method, and the average error of x-y coordinate position result as (9.25,3.65) is better. From the experiment, the designed robot can help the visually impaired people to indoor navigation in unknown indoor environment. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Robots

来源：评论

学校读者我要写书评

暂无评论

Trade-off Analysis of Gateway Strategies for Embedding of CAN Frames into Ethernet Packets 15

Trade-off Analysis of Gateway Strategies for Embedding of CA...

引用

15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Bae, Sungwon Park, Hanyoung Ko, Kanghyun Choi, Ji-Woong Dgist Department of Electrical Engineering and Computer Science Daegu Korea Republic of Cheonan Korea Republic of

ISBN: (纸本)9798350364637

The advancement of advanced driver assistance systems (ADAS) has increased the demand for more sensor data within vehicles, necessitating faster data transmission. To meet this requirement, in-vehicle networks (IVNs) with higher link speeds are essential, as the data rates of traditional IVN protocols are insufficient for future applications. To address these demands, an IVN architecture that integrates both controller area network (CAN) and automotive Ethernet is being considered. The coexistence of CAN and automotive Ethernet necessitates the use of gateways, which introduces latency when transferring data from CAN to automotive Ethernet. However, many studies have not thoroughly explored the strategies and characteristics of such gateways and their impact on performance. Therefore, in this paper, we analyze the processing latency and network efficiency associated with different gateway strategies for embedding CAN frames into Ethernet packets. The findings of this study show the trade-off between latency and network efficiency and highlight the potential for optimizing CAN-Ethernet gateways. © 2024 IEEE.

关键词： Advanced driver assistance systems

来源：评论

学校读者我要写书评

暂无评论

Verification and Design of Robust and Safe Neural Network-enabled Autonomous Systems 59

Verification and Design of Robust and Safe Neural Network-en...

引用

59th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2023

作者： Zhu, Qi Li, Wenchao Huang, Chao Chen, Xin Zhou, Weichao Wang, Yixuan Li, Jiajun Fu, Feisi Northwestern University Department of Electrical and Computer Engineering United States Boston University Department of Electrical and Computer Engineering United States University of Liverpool Department of Computer Science United Kingdom University of New Mexico Department of Computer Science United States

ISBN: (纸本)9798350328141

Neural networks are being applied to a wide range of tasks in autonomous systems, such as perception, prediction, planning, control, and general decision making. While they may improve system performance over traditional physical model-based methods, pressing concerns have been raised on the uncertain behaviors of neural networks under varying inputs, especially for safety-critical systems such as autonomous vehicles and robots. In this paper, we will discuss the challenges in ensuring the safety and robustness of neural network-enabled autonomous systems, and present our recent work in addressing these challenges. These include methods for certifying the robustness of neural networks, verifying the safety of neural network-controlled systems, designing these systems with safety assurance, and conducting safety-assured runtime adaptation. © 2023 IEEE.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Bilinear Attention Based Learning on Graphs

Bilinear Attention Based Learning on Graphs

引用

2024 IEEE International Conference on Big Data, BigData 2024

作者： Njanko, Adrien Rawat, Danda B. Howard University Dept. Electrical Engineering and Computer Science WashingtonDC United States

ISBN: (纸本)9798350362480

The attention mechanism is a core module in today's well-established models like Transformers and Graph Attention Networks (GAT). The attention coefficients needed typically requires heavy computational resources for stability and better predictions. This requirement limits the size of the sequence or graph a model can learn from, leading to separating connected graphs or sequences to ease training. In this paper, we introduce an attention mechanism that leverages Bilinear forms to handle sequences of arbitrary length, offering greater flexibility and scalability. This improvement not only optimizes computational resources but also offers better model performance. Furthermore, our Bilinear Attention Mechanism (BAM) can capture more complex relationships between nodes for less parameters count. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Electromagnetic Wave Propagation and Amplification in Anisotropic Material Made of Stacking Layers of Two-dimensional Material and Dielectric

Electromagnetic Wave Propagation and Amplification in Anisot...

引用

2024 United States National Committee of URSI National Radio science Meeting, USNC-URSI NRSM 2024

作者： Hakimi, Amin Rouhi, Kasra Capolino, Filippo Department of Electrical Engineering and Computer Science University of California IrvineCA92697 United States

ISBN: (纸本)9781946815194

Over the past few years, achieving gain in the terahertz (THz) regime has become increasingly significant for a variety of applications, including communications and sensing. Additionally, two-dimensional (2D) materials can exhibit extraordinary electromagnetic properties such as non-reciprocity, etc. We are interested in analyzing the propagation of waves in an anisotropic medium with gain induced by a transverse biasing voltage to achieve wave amplification at THz by stacking layers of 2D material and dielectric. © 2024 IEEE. All rights reserved.

关键词：

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：