检索结果-内蒙古大学图书馆

2023 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2023

作者： Sheet, Dalya Khalid Sultan, Nawar A. Aboosh, Omar Shamil Ahmed Ministry of Education Laboratory of Computer Science Mosul Iraq Northern Technical University Department of Computer Science Mosul Iraq University of Mosul Department of Basic Science Mosul Iraq

ISBN: (纸本)9798350307771

Intelligent Transportation System (ITS) was developed to increase traffic security and preserve the environment. Nowadays, a lot of focus is being placed on Vehicular Ad hoc Network (VANET) technology to meet the evolving and new requirements of ITS applications. Nonetheless, in order to promote ITS applications, the determination of an optimal route becomes critical, which in turn relies on effective Geographic-based Routing Protocols (GRPs). Determination of an optimal route toward the intended destination in a wide-range traffic network such as an urban environment is extremely important. Existing route discovery mechanisms generate high delays, particularly in areas containing a wide range of junctions and feeder roads. Thus, a new mechanism for reducing the size of the search area is required, which can avoid searching in a wide range of junctions and feeder roads. In this paper, the issue of the Rectangle Restricted Searching Area (RRSarea) algorithm is discussed, which aims to reduce the size of the searching area in a wide-range traffic network for optimal route determination. The issue has been resolved by proposing an Improved Rectangle Restricted Searching Area ( RRSarea ) algorithm that can control the size of ellipse by widening and narrowing the search region to incorporate an adequate number of junctions and feeder roads. Thus, the optimal route in terms of low end-to-end delay can be determined. Analysis of the results has revealed that the proposed algorithm outperforms RRSarea algorithm by considering two scenarios ranging between 0.3-1.2 km and 1.5-6.5 km for short and long distances. The results have clearly shown that the acceptable searching area size of the IRRSarea algorithm was increased, i.e. it reached the range of 0.38-3.79 km2 whereas it lowered and reached the range of 4.02-55.38 km2 for both small and large distances respectively. © 2023 IEEE.

关键词： Routing protocols

来源：评论

学校读者我要写书评

暂无评论

Solving Mystery Planning Problems Using Category Theory, Functors, and Large Language Models 3

Solving Mystery Planning Problems Using Category Theory, Fun...

引用

3rd International Conference on Assured Autonomy, ICAA 2024

作者： Jha, Sumit Kumar Jha, Susmit Ewetz, Rickard Velasquez, Alvaro Florida International University Department of Computer Science MiamiFL United States Sri International Computer Science Laboratory Menlo ParkCA United States University of Florida Ece Department GainesvilleFL United States University of Colorado Boulder Computer Science Department BoulderCO United States

ISBN: (纸本)9798331521011

Large language models (LLMs) have shown remarkable capabilities in natural language understanding and generation. However, they face challenges in solving complex planning problems, especially those obscured by altered terminologies and representations, known as mystery planning problems. This paper presents a novel approach leveraging category theory and functors to systematically map mystery planning problems to their canonical forms, enabling effective planning solutions. We demonstrate our methodology using the Mystery Blocks World domain, showcasing significant improvements in planning accuracy and efficiency. Contemporary LLMs, such as GPT-4 and Claude-3.5 Sonnet, conjecture the canonical form and the corresponding functor only by observing the structure of the mystery planning problem, and the resulting approach improves the accuracy of the LLM-based planning solution to 60% for problems with 4 blocks. © 2024 IEEE.

关键词： Problem solving

来源：评论

学校读者我要写书评

暂无评论

Improved Electric Load Prediction with Transfer Learning and Temperature Data 7

Improved Electric Load Prediction with Transfer Learning and...

引用

7th International Conference on Informatics and Applied Mathematics, IAM 2024

作者： Farah, Rania Farou, Brahim Kouahla, Zineddine Seridi, Hamid University Guelma Department of Computer Science LabStic Laboratory 8 Mai 1945 Guelma24000 Algeria

Accurate prediction of electrical load is crucial for optimising energy management, especially in areas where temperature fluctuations directly influence energy demand. This study proposes an innovative approach by applying transfer learning to improve the accuracy of electrical load predictions by integrating a temperature database. A pre-trained base model is fitted to local temperature data to take advantage of the relationship between climatic conditions and load variations. Results show that this method offers significant performance gains by reducing prediction errors while optimising training time resources. The proposed approach opens up interesting prospects for resource-constrained environments and use cases requiring fast, reliable predictions. © 2022 Copyright for this paper by its authors.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

On the Attractive and Repulsive Forces of Generalized Stochastic Neighbor Embedding With Alpha-Divergence

引用

IEEE Access 2024年 12卷 90380-90394页

作者： Lin, Hsin-Yi Tseng, Huan-Hsin Chien, Jen-Tzung Seton Hall University Department Of Mathematics And Computer Science South OrangeNJ07079 United States Brookhaven National Laboratory Computational Science Initiative UptonNY11973 United States Institute Of Electrical And Computer Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan

Stochastic neighbor embedding (SNE) performs nonlinear transformation from high-dimensional observation space to low-dimensional latent space which preserves neighbor affinities. Data pairs in latent space tend to be crowded due to the dimensionality reduction. To mitigate the crowding problem, certain characteristics are favorable in the design of the SNE setting. This study presents a fundamental analysis of SNE that not only generalizes the previous SNEs but also provides a systematic way to understand the intrinsic properties. From the perspective of theoretical connection, we are able to conceive a new generalized SNE (g-SNE) by introducing a regularized power-law distribution with the α-divergence for manifold learning. The proposed method generalizes and incorporates various favorable features for the clustering process. In addition, the proposed method provides high flexibility, admitting tailored realizations to properly reflect the similarity between original and dimension-reduced samples. Experiments are performed to analyze the proposed method, and its effectiveness is demonstrated with several learning tasks. © 2024 The Authors.

关键词： Data visualization

来源：评论

学校读者我要写书评

暂无评论

Bipartite synchronization of stochastic coupled systems with hybrid time-varying delays via asynchronous impulsive control

引用

IEEE Transactions on Automation science and Engineering 2025年 22卷 15778-15791页

作者： Liu, Yan Sun, Yiliang Zhou, Hui Tiangong University Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems School of Computer Science and Technology Tianjin300387 China Department of Mathematics Weihai264209 China Fuzhou University College of Computer and Data Science Fuzhou350108 China

This paper investigates the bipartite synchronization of stochastic coupled systems with hybrid time-varying delays and Markov jump via asynchronous impulsive control. Unlike existing studies, the asynchronous impulses considered in this paper are decentralized, which allows for the investigation of a wider range of dynamic properties. Additionally, to address asynchronous impulsive control, asynchronous average dwell-time condition is introduced for the first time, and new auxiliary functions are defined. Several sufficient criteria for attaining bipartite synchronization are derived through the application of the Lyapunov method and graph theory. In addition, the theoretical findings are applied to single-link robot arms with hybrid time-varying delays. To demonstrate the practicality of the findings, this paper includes two numerical examples. © 2004-2012 IEEE.

关键词： Stochastic control systems

来源：评论

学校读者我要写书评

暂无评论

Autonomous Driving Through Deep Learning in Video Games: A Visual-Based Perception and Action Approach 19

Autonomous Driving Through Deep Learning in Video Games: A V...

引用

19th Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2023

作者： Kastellos, Anestis Gkelios, Socratis Chatzichristofis, Savvas A. Cheng, Pericles Leng European University of Cyprus Department of Computer Science and Engineering Nicosia Cyprus Democritus University of Thrace Department of Electrical and Computer Engineering Xanthi Greece Neapolis University Pafos Is Laboratory Department of Computer Science Pafos Cyprus

ISBN: (纸本)9798350346497

Recent studies have demonstrated the utility of simulations and video games as viable approximations for real-world problems, particularly in autonomous driving. Initial testing and validation of autonomous systems are typically conducted in simulated environments, as they effectively mimic human behavior's visual-based perception and action. This paper presents a deep-learning model for autonomous driving in a video game that can close the loop between sensing and actuation. Our algorithm utilizes a pre-trained deep neural network to process images of the game environment and output the optimal driving commands. We use only visual information from the game, without additional processed data, and present an extensive comparison of model variations. Our approach makes a valuable contribution to the field of autonomy by showing that it can achieve responsive and realistic driving behavior using only visual input. By leveraging the power of video games and deep learning, autonomous driving can be significantly improved, making transportation safer and more reliable. © 2023 IEEE.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

Enhancing Self-regulated Learning Through an Emotional Chatbot: A Conceptual Framework 9th

Enhancing Self-regulated Learning Through an Emotional Chatb...

引用

9th International Conference on Smart City Applications, SCA 2024

作者： El Mourabit, Imane Jai Andaloussi, Said Ouchetto, Ouail Miyara, Mounia Computer Science and Systems Laboratory Department of Mathematics and Computer Sciences Faculty of Sciences Ain Chock Hassan II University of Casablanca Casablanca Morocco

ISBN: (纸本)9783031886522

Artificial Intelligence (AI) chatbots play an important role in modernizing education, particularly e-learning platforms by acting as intelligent tools to address issues such as disengagement and dropout rates. AI chatbots are also used to captivate students through engaging and enjoyable learning activities. These chatbots can enhance the pedagogical concept of Self-Regulated Learning (SRL) by providing personalized support and feedback to students. This work explores how AI chatbots might increase students’ SRL skills and foster successful learning outcomes in e-learning environments. Our proposed conceptual framework aims to link the AI chatbots (technological side) and self-regulated learning (pedagogical side) using the power of academic emotions. In the forethought phase of SRL, our AI chatbot will guide students in setting short-term goals while detecting their initial motivation and then monitor students in the following phases of the SRL process. The framework highlights how AI chatbots using academic emotions can be used to support learning needs and will serve as a roadmap for future theory development and empirical investigation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Data-driven electrical resistance tomography for robotic large-area tactile sensing

引用

science China(Information sciences) 2024年第9期67卷 336-337页

作者： Wendong ZHENG Huaping LIU Xiaofeng LIU Fuchun SUN School of Electrical Engineering and Automation Tianjin University of Technology Department of Computer Science and Technology Tsinghua University Key Laboratory of Maritime Intelligent Cyberspace Technology (Hohai University) Ministry of Education

Tactile sensing plays a crucial role in enabling robots to safely interact with objects in dynamic environments [1].Given that potential physical contact can occur at any location during robot interaction, there is a ... 详细信息

关键词： Robot applications

来源：评论

学校读者我要写书评

暂无评论

UformPose: A U-Shaped Hierarchical Multi-Scale Keypoint-Aware Framework for Human Pose Estimation

引用

IEEE Transactions on Circuits and Systems for Video Technology 2023年第4期33卷 1697-1709页

作者： Wang, You-Jie Luo, Yan-Min Bai, Gui-Hu Guo, Jing-Ming Huaqiao University Key Laboratory for Computer Vision and Pattern Recognition The College of Computer Science and Technology Xiamen361021 China National Taiwan University of Science and Technology Department of Electrical Engineering Taipei10607 Taiwan

Human pose estimation is a fundamental yet challenging task in computer vision. However, difficult scenarios such as invisible keypoints, occlusions and small-scale persons are still not well-handed. In this paper, we present a novel pose estimation framework named UformPose which targets to relieve these issues. UformPose has two core designs: Shared Feature Pyramid Stem (SFPS) and U-shaped hierarchical Multi-scale Keypoint-aware Attention Module (U-MKAM). SFPS is a feature pyramid stem with shared mechanism to learn stronger low-level features at the initial stage, and the shared mechanism can facilitate cross-resolution commonality learning. Our U-MKAM attempts to generate high-quality high-resolution representations by integrating all levels of feature representation of the backbone layer by layer. More importantly, we utilize the flexibility of attention operations for keypoint-aware modeling, which explicitly captures and trades-offs the dependencies between keypoints. We empirically demonstrate the effectiveness of our framework through the competitive pose estimation results on the COCO dataset. Extensive experiments and visual analysis on CrowdPose demonstrate the robustness of our model in crowd scenes. © 1991-2012 IEEE.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

Optimizing Smart Home Energy Forecasting: Unveiling Superiority among RBFNN, ESN, and LSTM 4

Optimizing Smart Home Energy Forecasting: Unveiling Superior...

引用

4th International Conference on Innovative Research in Applied science, Engineering and Technology, IRASET 2024

作者： Bhih, Mouad Abou Elassad, Zouhair Elamrani Ameksa, Mohammed Boustani, Abdelhakim El El Meslouhi, Othmane Abou Elassad, Dauha Elamrani Cadi Ayyad University Sars Research Team Ensas Computer Science Department Safi Morocco Fssm Cadi Ayyad University Lisi Laboratory Computer Science Department Morocco Moroccan School Of Engineering Sciences Emsi Lamigep Laboratory Marrakech Morocco

ISBN: (纸本)9798350309508

In response to the accelerating integration of IoT devices for monitoring residential energy consumption, this study focuses on identifying robust predictive models aligned with global sustainability goals. Investigating three advanced models-Radial Basis Function Neural Networks (RBFNN), Echo State Networks (ESN), and Long Short-Term Memory (LSTM)-reveals distinct performance metrics. RBFNN demonstrates solid performance, while after meticulous tuning, ESN exhibits superior predictive capabilities, outperforming traditional techniques and even LSTM. Employing Recursive Feature Elimination (RFE) identifies key predictors, emphasizing ESN's heightened predictive accuracy. This research provides empirical insights, highlighting ESN's potential in enabling sustainable energy management within smart homes. © 2024 IEEE.

关键词： Long short-term memory

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：