检索结果-内蒙古大学图书馆

2022 Research, Invention, and Innovation Congress: Innovative Electricals and electronics, RI2C 2022

作者： Chudpooti, Nonchanutt Sangpet, Patchadaporn Chudpooti, Sukanya Akkaraekthalin, Prayoot Robertson, Ian D. Somjit, Nutapong King Mongkut's University of Technology North Bangkok Facutly of Appied Science Department of Industrial Physics and Medical Instrumentation Bangkok Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Deparment of Electrical and Computer Engineering Bangkok Thailand University of Leeds School of Electronic and Electrical Engineering Leeds United Kingdom

ISBN: (数字)9781665473248

ISBN: (纸本)9781665473248

This paper presents a 3D-printed hemispherical lens incorporated with a square patch microstrip antenna for liquid-mixture characterization. The proposed hemispherical lens antenna is designed, fabricated, and integrated with the microstrip patch planar antenna. An Acrylonitrile Butadiene Styrene (ABS) is selected to design the 3D-printed lens antenna by using the fused deposition modeling (FDM) method, due to available 3D-printed material in the laboratory. The optimum dimensions and shape of the hemispherical lens antenna are obtained by using the 3D EM Simulation CST Studio, which is used to investigate the characteristic of the antenna, e.g., gain, radiation pattern, and reflection coefficient. To characterize the liquid content in NaCl solution, the level of the transmission coefficient (S21) is detected. The proposed sensor system offers various preferable features, e.g., non-destructive method and non-contact measurement. Five liquid solutions under test (LUT), e.g., 5%, 10%, 15%, 20%, and 25% NaCl in the NaCl-aqueous solutions, are measured and performed to generate the extraction model. The proposed sensor also offers other advantages, e.g., real-time monitoring and no life-cycle limitation. © 2022 IEEE.

关键词： Microstrip antennas

来源：评论

学校读者我要写书评

暂无评论

George J. Boole: A Nineteenth Century Man for the Modern Digital Era 18th

George J. Boole: A Nineteenth Century Man for the Modern Dig...

引用

18th International Conference on computer Aided Systems Theory, EUROCAST 2022

作者： Stanković, Radomir S. Stanković, Milena Astola, Jaakko Moraga, Claudio Mathematical Institute of SASA Belgrade Serbia Faculty of Electronic Engineering Niš Serbia Tempere University of Technology Tampere Finland Faculty of Computer Science Technical University of Dortmund Dortmund Germany Department of Informatics Technical University "Federico Santa María" Valparaíso Chile

ISBN: (纸本)9783031253119

It is entirely justified to use the attribute digital in describing contemporary era due to omnipresence of digital technologies and devices based on them and their strong influence to almost all aspects of human activities. The present paper is a tribute to a man whose work in logic and mathematics, leading to the mathematical logic, set theoretical foundations for the development and establishing of digital era. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Digital devices

来源：评论

学校读者我要写书评

暂无评论

Stock Market Trading Based on Market Sentiments and Reinforcement Learning

引用

computers, Materials & Continua 2022年第1期70卷 935-950页

作者： K.M.Ameen Suhail Syam Sankar Ashok S.Kumar Tsafack Nestor Naglaa F.Soliman Abeer D.Algarni Walid El-Shafai Fathi E.Abd El-Samie Department of Computer Science&Engineering NSS College of EngineeringPalakkad678008KeralaIndia Department of Electronics and Communication Engineering NSS College of EngineeringPalakkad678008KeralaIndia Unitéde Recherche de Matière Condensée D’Electronique et de Traitement du Signal(URAMACETS)Department of PhysicsUniversity of DschangP.O.Box 67DschangCameroon Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Stock market is a place,where shares of different companies are *** is a collection of buyers’and sellers’*** this digital era,analysis and prediction in the stock market have gained an essential role in shaping today’s *** market analysis can be either fundamental or *** analysis can be performed either with technical indicators or through machine learning *** this paper,we report a system that uses a Reinforcement Learning(RL)network and market sentiments to make decisions about stock market *** system uses sentiment analysis on daily market news to spot trends in stock *** sentiment analysis module generates a unified score as a measure of the daily news about *** score is then fed into the RL module as one of its *** RL section gives decisions in the form of three actions:buy,sell,or *** objective is to maximize long-term future *** have used stock data of Apple from 2006 to 2016 to interpret how sentiments affect *** stock price of any company rises,when significant positive news become available in the public *** results reveal the influence of market sentiments on forecasting of stock prices.

关键词： Deep learning machine learning daily market news reinforcement learning stock market

来源：评论

学校读者我要写书评

暂无评论

Efficient Deep Post-Decision State Learning for Privacy-Conscious Offloading in MEC-Enabled 6G Networks

Efficient Deep Post-Decision State Learning for Privacy-Cons...

引用

Emerging Technologies for Dependable Internet of Things (ICETI), International Conference on

作者： Mamoon M. Saeed Rania A. Mokhtar Mohammed S. Elbasheir Elmustafa Sayed Ali Zeinab E. Ahmed Mayada A. Ahmed Department of Communications and Electronics Engineering University of Modern Sciences (UMS) Yemen Faculty of Engineering School of Elecs Eng. Sudan University of Science and Technology Khartoum Sudan School of Electronic Engineering Sudan University of Science and Technolog Khartoum Sudan Department of Electrical & Electronic Engineering Faculty of Engineering Red Sea University Sudan Department of Electrical and Computer International Islamic University Malaysia

ISBN: (数字)9798331533557

ISBN: (纸本)9798331533564

With the growing demand for data-intensive applications, offloading tasks to multi-access edge computing (MEC) servers has become essential. However, privacy concerns arise as sensitive data is transmitted to remote servers. To address this, the proposed method uses deep learning techniques to intelligently analyze and classify data packets, ensuring that only non-sensitive information is offloaded. This paper proposes a novel approach called a deep post-decision state (PDS) learning privacy-concerned offloading in MEC-enabled 6G Environments. A Post-Decision State (PDS) approach that selectively selects data packets while maintaining privacy is used to train the deep learning model. The suggested method successfully balances the trade-off between increasing system performance and safeguarding user privacy to achieve privacy-aware offloading in MEC-enabled 6G networks, as shown by the experimental findings. Also, it performs noticeably better than the traditional DQN, according to simulation data.

关键词： 6G mobile communication Deep learning Performance evaluation Privacy Data privacy Multi-access edge computing System performance Servers Internet of Things Energy harvesting

来源：评论

学校读者我要写书评

暂无评论

Vision Reimagined: AI-Powered Breakthroughs in WiFi Indoor Imaging

arXiv

引用

arXiv 2024年

作者： Shi, Jianyang Zhang, Bowen Dubey, Amartansh Murch, Ross Jing, Liwen Faculty of Information and Intelligence Shenzhen X-Institute Shenzhen China College of Big Data and Internet Shenzhen Technology University Shenzhen China Department of Electronic and Computer Engineering the Hong Kong University of Science and Technology Hong Kong School of Computer Science and Technology Harbin Institute of Technology Shenzhen China

Indoor imaging is a critical task for robotics and internet-of-things. WiFi as an omnipresent signal is a promising candidate for carrying out passive imaging and synchronizing the up-to-date information to all connected devices. This is the first research work to consider WiFi indoor imaging as a multi-modal image generation task that converts the measured WiFi power into a high-resolution indoor image. Our proposed WiFi-GEN network achieves a shape reconstruction accuracy that is 275% of that achieved by physical model-based inversion methods. Additionally, the Fréchet Inception Distance score has been significantly reduced by 82%. To examine the effectiveness of models for this task, the first large-scale dataset is released containing 80,000 pairs of WiFi signal and imaging target. Our model absorbs challenges for the model-based methods including the non-linearity, ill-posedness and non-certainty into massive parameters of our generative AI network. The network is also designed to best fit measured WiFi signals and the desired imaging output. For reproducibility, we will release the data and code upon acceptance. Copyright © 2024, The Authors. All rights reserved.

关键词： Inverse problems

来源：评论

学校读者我要写书评

暂无评论

Episodic memory based continual learning without catastrophic forgetting for environmental sound classification

引用

Journal of Ambient Intelligence and Humanized Computing 2023年第4期14卷 4439-4449页

作者： Karam, Said Ruan, Shanq-Jang Haq, Qazi Mazhar ul Li, Lieber Po-Hung Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Sec. 4 No. 43 Keelung Rd Taipei10607 Taiwan Military College of Signals National University of Sciences and Technology Islamabad Pakistan Faculty of Medicine and Institute of Brain Science National Yang-Ming Chiao-Tung University Taipei Taiwan Department of Otolaryngology Cheng Hsin General Hospital Taipei Taiwan Department of Medical Research China Medical University Hospital China Medical University Taichung Taiwan

Convolutional neural network suffers from catastrophic forgetting during continual learning. This is one of the major obstacles for artificial intelligence, to solve new problems without forgetting the previously learned information. In this article, we propose an episodic memory technique for learning sound data incrementally. The proposed method observes tasks sequentially and successfully solves the new task without forgetting the previous task. The results show that the proposed method is able to transfer backward and forward knowledge efficiently. The performance evaluation demonstrates that the proposed method achieves better performance than other benchmarks. For ESC-50 and UrbanSound8K datasets, the proposed method obtained 96.5% and 93.1% accuracy, respectively. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Benchmarking

来源：评论

学校读者我要写书评

暂无评论

Proposing Algorithm to Localize and Extract Facial Information Using FaceNet and MTCNN

Proposing Algorithm to Localize and Extract Facial Informati...

引用

2022 International Conference on Data Analytics for Business and Industry, ICDABI 2022

作者： Huu, Phat Nguyen Thi, Anh Pham Danh, Quy Mai Quynh, Nhi Chu Hoang, Tran Manh Minh, Quang Tran Hanoi University of Science and Technology School of Electrical and Electronic Engineering Hanoi Viet Nam Hanoi-Amsterdam Highschool for the Gifted 11 English 1 Classs Hanoi Viet Nam Faculty of Computer Science and Engineering Hochiminh Viet Nam Hochiminh Viet Nam

ISBN: (纸本)9781665490580

Facial recognition systems from photos or videos are widely applied in daily life such as surveillance systems, access management, and security investigations. It can be compared with other forms of biometrics such as fingerprint or iris recognition systems. Among the facial recognition systems developed so far, Facenet introduced by Google brings a high accuracy rate. Therefore, in this paper, we will build a face recognition system using MTCNN| to detect faces and extract features using Facenet and SVM to classify and recognize faces. The proposed algorithm consists of 5 steps, namely, preparing data for training;face detection from data with MTCNN;extracting the features of each face with the FaceNet Keras model;classifying feature vectors by SVM, and performing face recognition. The algorithm results in an accuracy of up to 99.63% with data from only 5 to 15 images for a person. This proves that the algorithm is feasible when applied in practice. © 2022 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Optimizing Convolutional Neural Networks for Plant Disease Detection on ARM-M Microcontrollers

Optimizing Convolutional Neural Networks for Plant Disease D...

引用

Agrosystem engineering, technology & Applications (AGRETA), IEEE International Conference on

作者： Hermawan Nugroho Chew Jing Xan Tay Fei Siang Sivaraman Eswaran Department of Electrical and Electronic Engineering University of Nottingham Malaysia Semenyih Malaysia Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Campus Sarawak Malaysia Department of Electrical and Computer Engineering Curtin University Malaysia Miri Malaysia

ISBN: (数字)9798350373660

ISBN: (纸本)9798350373677

Rice production in Malaysia is facing significant challenges due to plant diseases and environmental hazards, leading to a decline in the rice self-sufficiency ratio. To address these issues, this study explores the deployment of AI-driven plant disease detection models on ARM-M based microcontrollers, specifically the STM32H747I-DISCO Discovery kit. Utilizing Convolutional Neural Networks (CNNs) for image recognition, we aim to achieve real-time disease monitoring without relying on continuous internet connectivity. Our research evaluates five state-of-the-art models-FD-MobileNet, MobileNet V1, MobileNet V2, ResNet V1, and SqueezeNet V1-focusing on their suitability for resource-constrained environments. The results indicate that FD-MobileN et is the most resource-efficient model with the lowest number of Multiply-Accumulate operations (MACCs) and the shortest inference time, achieving an accuracy rate of 98.44%. However, SqueezeNet, despite having the highest accuracy at 99.22%, faces deployment challenges due to its large model size on the STM32H747I-DISCO board. This work highlights the potential of embedded AI applications in enhancing agricultural resilience and efficiency.

关键词： Plant diseases Accuracy Microcontrollers Computational modeling Crops Real-time systems Agriculture Convolutional neural networks Testing Synthetic data

来源：评论

学校读者我要写书评

暂无评论

So Far Yet So Near: Time Series Data Augmentation with Exploring non-Semantic Boundaries based on Reinforcement Learning

So Far Yet So Near: Time Series Data Augmentation with Explo...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Haoran Li Zhibo Zhang Jiarong Kang Xun Jiang Xiaoli Gong Jin Zhang Zhe Sun Andrzej Cichocki College of Computer Science Nankai University Tianjin China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Medicine and Faculty of Health Data Science Juntendo University Japan Polish Academy of Science Systems Research Institute Warsaw Poland

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Data augmentation effectively expands feature distribution in time series classification, enhancing downstream task performance. However, existing techniques often fail to maintain semantic consistency between augmented and original time series data, causing label noise and thereby degrading downstream task performance. We argue that data augmentation should preserve time series semantic consistency and expand the non-semantic information space. In this paper, we reformulate data augmentation as a semantic path planning problem between original data and augmented data, modeled as a Markov Decision Process (MDP). We propose a reinforcement learning-based algorithm (RL) named FreqSYN, where the action space is defined by a set of learnable Gaussian kernels that perturbs the frequency domain of the original data to generate augmented samples. The confidence coefficients of augmented data in semantically relevant classification tasks are used as a reward to iteratively refine the FreqSYN. Our method is validated across four datasets, achieving state-of-the-art performance, with a 2% improvement in F1 score over the SimPSI method. The code and models are available at https://***/NKU-EmbeddedSystem/FreqSYN.

关键词： Semantics Time series analysis Signal processing algorithms Reinforcement learning Data augmentation Prediction algorithms Path planning Data models Kernel Speech processing

来源：评论

学校读者我要写书评

暂无评论

Establish seedling quality classification standard for Chrysanthemum efficiently with help of deep clustering algorithm

arXiv

引用

arXiv 2024年

作者： Jing, Yanzhi Zhao, Hongguang Yu, Shujun College of Horticulture South China Agricultural University GuangDong Guangzhou China School of Computer Science and Technology Faculty of Electronic and Information Engineering Xi’an Jiaotong University Shaanxi Xi’an China

Establishing reasonable standards for edible chrysanthemum seedlings helps promote seedling development, thereby improving plant quality. However, current grading methods have the several issues. The limitation that only support a few indicators causes information loss, and indicators selected to evaluate seedling level have a narrow applicability. Meanwhile, some methods misuse mathematical formulas. Therefore, we propose a simple, efficient, and generic framework, SQCSEF, for establishing seedling quality classification standards with flexible clustering modules, applicable to most plant species. In this study, we introduce the state-of-the-art deep clustering algorithm CVCL, using factor analysis to divide indicators into several perspectives as inputs for the CVCL method, resulting in more reasonable clusters and ultimately a grading standard Scvcl for edible chrysanthemum seedlings. Through conducting extensive experiments, we validate the correctness and efficiency of the proposed SQCSEF framework. Copyright © 2024, The Authors. All rights reserved.

关键词： Factor analysis

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：