检索结果-内蒙古大学图书馆

Incomplete and Asymmetric Information in General Lotto Games

IEEE Transactions on Automatic Control 2024年第6期70卷 3617-3632页

作者： Paarporn, Keith Chandan, Rahul Alizadeh, Mahnoosh Marden, Jason R. University of Colorado Department of Computer Science Colorado Springs United States Amazon Robotics United States University of California Department of Electrical and Computer Engineering Santa BarbaraCA United States

In this paper, we consider incomplete and asymmetric information formulations of the General Lotto game, which describes two opposing players that strategically allocate limited resources over multiple contests. In particular, we consider scenarios where one of the player's resource budget is common knowledge while the other player's is private. Our main contribution provides complete equilibrium characterizations in the scenario where the private resource budget is drawn from a Bernoulli distribution. We then leverage these characterizations to analyze a resource assignment problem where a commander must decide how to assign resources to sub-colonels that compete against opponents in separate General Lotto games. While optimal deterministic policies have previously been characterized in the literature, we broaden the context by deriving optimal randomized policies, which induce asymmetric information General Lotto games. Leveraging our equilibrium characterizations, we demonstrate that randomization can offer a four-fold improvement in the commander's performance over the optimal deterministic assignments. © 1963-2012 IEEE.

关键词： Budget control

来源：评论

学校读者我要写书评

暂无评论

A trajectory-level robot skill learning method based on fuzzy systems

引用

science China(Information sciences) 2025年第5期68卷 38-52页

作者： Chao ZENG Zhen DENG Boyang TI Yongsheng GAO Department of Computer Science University of Liverpool School of Mechanical Engineering and Automation Fuzhou University State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences State Key Laboratory of Robotics and System Harbin Institute of Technology

Learning from demonstration(LfD) allows for the effective transfer of human manipulation skills to a robot by building a model that represents these skills based on a limited number of demonstrated ***,a skilllearning model that can comprehensively satisfy multiple requirements,such as computational complexity,modeling accuracy,trajectory smoothness,and robustness,is still ***,this work aims to provide such a model by employing fuzzy ***,we introduce an LfD model named Takagi-Sugeno-Kang fuzzy system-based movement primitives(TSKFMPs),which exploits the advantages of the fuzzy theory for effective robotic imitation learning of human *** work formulates the TSK fuzzy system and gradient descent(GD) as imitation learning models,leveraging recent advancements in GD-based optimization for fuzzy *** study takes a two-step strategy.(ⅰ) The input-output relationships of the model are established using TSK fuzzy systems based on demonstration *** this way,the skill is encoded by the model parameter in the latent space.(ⅱ) GD is used to optimize the model parameter to increase the modeling accuracy and trajectory *** further explain how learned trajectories are adapted to new task scenarios through local *** conduct multiple tests using an open dataset to validate our method,and the results demonstrate performance comparable with those of other ***,we implement it in a real-world case study.

关键词： robot skill learning movement primitives TSK fuzzy systems gradient descent robotics

来源：评论

学校读者我要写书评

暂无评论

Heat Transfer Area Optimization for Heat Exchanger System

引用

computer Modeling in engineering & sciences 2025年第4期143卷 335-349页

作者： Yu-Cheng Liao Fu-I Chou Po-Yuan Yang Jyh-Horng Chou Department of Electrical Engineering National Kaohsiung University of Science and TechnologyKaohsiung807Taiwan Department of Intelligent Robotics National Pingtung UniversityPingtung900Taiwan Department of Mechanical and Computer-Aided Engineering Feng Chia UniversityTaichung407Taiwan

This paper presents an allowable-tolerance-based group search optimization(AT-GSO),which combines the robust GSO(R-GSO)and the external quality design planning of the Taguchi ***-GSO algorithm is used to optimize the heat transfer area of the heat exchanger *** R-GSO algorithm integrates the GSO algorithm with the Taguchi method,utilizing the Taguchi method to determine the optimal producer in each iteration of the GSO algorithm to strengthen the robustness of the search process and the ability to find the global *** conventional parameter design optimization,it is typically assumed that the designed parameters can be applied accurately and consistently throughout ***,for systems that are sensitive to changes in design parameters,even minor inaccuracies can substantially reduce overall system ***,the permissible variations of the design parameters are considered in the tolerance-optimized design to ensure the robustness of the *** optimized design of the heat exchanger system assumes that the system’s operating temperature parameters are ***,fixing the systemoperating temperature parameters at a constant value is *** paper assumes that the system operating temperature parameters have an uncertainty error when optimizing the heat transfer area of the heat exchanger *** results show that the AT-GSO algorithm optimizes the heat exchanger system and finds the optimal operating temperature in the absence of tolerance and under three tolerance conditions.

关键词： Heat exchanger system allowable tolerance-based group search optimization Taguchi method tolerance design

来源：评论

学校读者我要写书评

暂无评论

i-Glove: An Intelligent Glove System Based on Deep Learning to Support Deaf-blind Individuals in Recognizing Banknotes 3

i-Glove: An Intelligent Glove System Based on Deep Learning ...

引用

3rd IEEE International Conference on robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Hossain, Fahad Mohammad Alavi, Rafit Safat, Yeamin Sarker, Gobinda Chandra Noor, Nafisa Hossain, Ekram Mymensingh Engineering College Department of Computer Science and Engineering Mymensingh Bangladesh University of Dhaka Department of Robotics and Mechatronics Engineering Dhaka Bangladesh

ISBN: (纸本)9798331534400

The deaf-blind community faces significant challenges in identifying banknotes due to simultaneous hearing and vision impairments, which complicate their ability to conduct daily financial transactions. Recent advances in Artificial Intelligence (AI)-based assistive technologies offer promising potential to address these challenges. This study presents the "i-Glove,"a wearable device specifically developed to assist the deaf-blind community in Bangladesh with accurate banknote identification. The i-Glove is designed to recognize nine common Bangladeshi banknotes (e.g., BDT 20, 50, 100) through a camera module integrated on the palmar side of the glove, which captures real-time images of the banknotes. These images are then processed by a convolutional neural network (CNN) model to accurately classify each banknote. Identified notes are translated into unique vibration patterns that are delivered to the user through a shaftless vibration motor embedded in the glove, providing intuitive, non-visual communication. Compared to previous approaches, our model demonstrates superior performance even with a compact dataset, achieving an overall F1-score accuracy of 96.55%, with class-specific precision, recall, and F1-score metrics ranging from 90% to 100%. Additionally, a dedicated application is developed which supports real-time banknote detection and interfaces seamlessly with the glove. The proposed system represents a significant step forward in assistive technology for the deaf-blind community, empowering the users with greater autonomy in financial transactions. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

TECMH:Transformer-Based Cross-Modal Hashing For Fine-Grained Image-Text Retrieval

引用

computers, Materials & Continua 2023年第5期75卷 3713-3728页

作者： Qiqi Li Longfei Ma Zheng Jiang Mingyong Li Bo Jin College of Computer and Information Science Chongqing Normal UniversityChongqing401331China Institute of Systems and Robotics(ISR) Department of Electrical and Computer Engineering(DEEC)University of CoimbraCoimbraPortugal

In recent years,cross-modal hash retrieval has become a popular research field because of its advantages of high efficiency and low ***-modal retrieval technology can be applied to search engines,crossmodalmedical processing,*** existing main method is to use amulti-label matching paradigm to finish the retrieval ***,such methods do not use fine-grained information in the multi-modal data,which may lead to suboptimal *** avoid cross-modal matching turning into label matching,this paper proposes an end-to-end fine-grained cross-modal hash retrieval method,which can focus more on the fine-grained semantic information of multi-modal ***,the method refines the image features and no longer uses multiple labels to represent text features but uses BERT for ***,this method uses the inference capabilities of the transformer encoder to generate global fine-grained ***,in order to better judge the effect of the fine-grained model,this paper uses the datasets in the image text matching field instead of the traditional label-matching *** article experiment on Microsoft COCO(MS-COCO)and Flickr30K datasets and compare it with the previous *** experimental results show that this method can obtain more advanced results in the cross-modal hash retrieval field.

关键词： Deep learning cross-modal retrieval hash learning transformer

来源：评论

学校读者我要写书评

暂无评论

Classification of Jackfruit Species Using Deep Learning Model 2

Classification of Jackfruit Species Using Deep Learning Mode...

引用

2nd IEEE International Conference on Recent Advances in Information Technology for Sustainable Development, ICRAIS 2024

作者： Veeresha, R.K. Karegoudra, Shilpa Kumar, Bhavin Pavan, Pavan Department of Robotics and Artificial Intelligence Udupi574110 India Department of Computer Science and Engineering Udupi574110 India

ISBN: (纸本)9798350354461

Identifying Jackfruit spices using a deep learning model involves leveraging advanced neural network architectures to classify spices accurately. The model is trained on a dataset containing images of various spices commonly used with Jackfruit, such as deng surya, manmohan, prakashchandra, vietnamearly, and others. Through multiple layers of convolutional neural networks (CNNs) and possibly recurrent neural networks (RNNs), the model learns intricate patterns and features within the images to differentiate between different species. Transfer learning techniques might also be employed to adapt pre-trained models to the specific task of spice identification. The model's performance is evaluated on a separate test dataset to measure its accuracy, precision, recall, and F1 score. The paper provides a comprehensive overview of the deep learning approach to identifying Jackfruit spices, highlighting its potential applications in culinary research, food industry automation, and dietary analysis. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

CredChain: Academic and Professional Certificate Verification System using Blockchain

CredChain: Academic and Professional Certificate Verificatio...

引用

2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems, iCACCESS 2024

作者： Al Ahmed, Saniyat Mamun Rudro, Rifat Al Prity, Afrina Jannat Saha, Suman Mansoor, Nafees Nur, Kamruddin Department of Computer Science Dhaka Bangladesh Bangabandhu Sheikh Mujibur Rahman Digital University Bangladesh Department of IoT and Robotics Engineering Gazipur Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350350289

The integrity of academic and professional creden-tials is a foundation in education and the professional fields. Traditional certificate verification methods are flawed by in-efficiencies, susceptibility to fraud, and a reliance on manual processes that compromise security and expedience. To address these issues, we present CredChain, a robust Academic and Professional Certificate Verification System that harnesses the power of blockchain technology. Our system utilizes Ethereum-based Decentralized Applications (DApps) and Smart Contracts integrated with the InterPlanetary File System (IPFS) to ensure immutable, secure, and transparent verification processes. We outline the novel algorithms for essential system functions such as certificate uploading, verification, and retrieval of certificates. The CredChain system has been subjected to rigorous testing and validation, demonstrating its effectiveness in mitigating the risk of fraudulent certificates and streamlining the verification process accurately. © 2024 IEEE.

关键词： Smart contract

来源：评论

学校读者我要写书评

暂无评论

Design of an Automatic Price Tag System for Web-Based Retail Business 9

Design of an Automatic Price Tag System for Web-Based Retail...

引用

9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Liawatimena, Suryadiputra Gunawan, Devina Bina Nusantara University Automotive & Robotics Program Computer Engineering Department BINUS ASO School of Engineering Computer Science Deparment BINUS Graduate Program Master of Computer Science Jakarta11480 Indonesia Bina Nusantara University Automotive & Robotics Program Computer Engineering Department BINUS ASO School of Engineering Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

Modern retail businesses face a significant challenge with the inefficiency of manually changing price labels on shelves. This manual process not only consumes valuable time and resources but also increases the likelihood of errors, leading to potential inaccuracies in pricing and a less streamlined shopping experience for customers. The proposed solution is the implementation of an Electronic Shelf Label (ESL) system that automatically displays the prices of goods on retail business shelves. This system connects a website (front end and back end) to e-paper via a Wi-Fi network and microcontroller, allowing retail business owners to update prices more easily. Additionally, buyers can search for desired items through the website. The results are the time to send data from the website to the e-paper, namely, to know the performance of the e-paper used. The average time required from 10 attempts to send data from the website to the e-paper is 20.935 seconds. Since the data is connected to the server and will be updated automatically, using this system will be more efficient than manually changing the price label, although it takes time to transfer data from the website to the e-paper. © 2024 IEEE.

关键词： Sales

来源：评论

学校读者我要写书评

暂无评论

ViT-SENet-Tom: machine learning-based novel hybrid squeeze–excitation network and vision transformer framework for tomato fruits classification

引用

Neural Computing and Applications 2025年第9期37卷 6583-6600页

作者： Swapno, S M Masfequier Rahman Nobel, S. M. Nuruzzaman Islam, Md Babul Bhattacharya, Pronaya Mattar, Ebrahim A. Department of CSE Bangladesh University of Business and Technology Dhaka1216 Bangladesh Department of Computer Modeling Electronic and System Engineering UNICAL Rende Italy Department of Computer Science and Engineering Amity School of Engineering and Technology and Research and Innovation Cell Amity University West Bengal Kolkata India Robotics - Cybernetic College of Engineering University of Bahrain Manama Bahrain

Tomatoes are essential fruits in numerous nations for their vast demand. It is very important to maintain the freshness of tomatoes. One of the primary challenges in the recent culinary landscape is accurately identifying healthy tomatoes while effectively eliminating damaged or rejected ones. Existing approaches employ various strategies for categorizing tomato fruit, but they often suffer from inaccuracies, slow detection, and suboptimal performance. Thus, motivated by this gap, in this paper, we propose a novel machine learning (ML) framework, ViT-SENet-Tom, which is a hybrid vision transformer (ViT) model with squeeze and excitation (SENet) block network for fast, accurate, and efficient tomato fruit classification. The framework works on three tomato classes, respectively, the ripe, unripe, and reject. In developing the proposed model, we utilized advanced and newly designed layers and functions. This integration created a more complex and sophisticated neural network, significantly enhancing efficiency and contributing to the model’s novelty. Our chosen dataset was small initially, but we implemented augmentation techniques to increase its size. This approach made our system more reliable, efficient, and effective. The hybrid ViT-SENet framework employs encoders and self-attention networks with squeeze and excitation channel functions to allow precise, robust, fast, and efficient tomato classification. In simulation, the framework achieves a training accuracy of 99.87% and validation accuracy of 93.87%, indicating the precise classification of tomatoes. Besides, this work tests accuracy using fivefold cross-validation. The highest accuracy seen at fold-5 is 99.90%. These testing results demonstrate the efficacy of the proposed framework in real-deployment scenarios. The implementation has the potential to provide enhanced and more sustainable food security and safety in future. © The Author(s) 2025.

关键词： Fruits

来源：评论

学校读者我要写书评

暂无评论

Support vector machine with discriminative low-rank embedding

引用

CAAI Transactions on Intelligence Technology 2024年第5期9卷 1249-1262页

作者： Guangfei Liang Zhihui Lai Heng Kong Computer Vision Institute College of Computer Science and Software EngineeringShenzhen UniversityShenzhenChina Shenzhen Institute of Artificial Intelligence and Robotics for Society ShenzhenChina Department of Breast and Thyroid Surgery BaoAn Central Hospital of ShenzhenShenzhenChina

Support vector machine(SVM)is a binary classifier widely used in machine ***,neglecting the latent data structure in previous SVM can limit the performance of SVM and its *** address this issue,the authors propose a novel SVM with discriminative low-rank embedding(LRSVM)that finds a discriminative latent low-rank subspace more suitable for SVM *** extension models of LRSVM are introduced by imposing different orthogonality constraints to prevent computational inaccuracies.A detailed derivation of the authors’iterative algorithms are given that is essentially for solving the SVM on the low-rank ***,some theorems and properties of the proposed models are presented by the *** is worth mentioning that the subproblems of the proposed algorithms are equivalent to the standard or the weighted linear discriminant analysis(LDA)*** indicates that the projection subspaces obtained by the authors’algorithms are more suitable for SVM classification compared to those from the LDA *** convergence analysis for the authors proposed algorithms are also ***,the authors conduct experiments on various machine learning data sets to evaluate the *** experiment results show that the authors’algorithms perform significantly better than other algorithms,which indicates their superior abilities on classification tasks.

关键词： iterative methods machine leaning support vector machunes

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：