检索结果-内蒙古大学图书馆

11th International Conference on Advanced Cloud and Big Data, CBD 2023

作者： Guo, Kai Luo, Hanjiang Tao, Hang Ruby, Rukhsana Qin, Zhizun Liu, Kui Shandong University of Science and Technology College of Computer Science and Engineering Qingdao China Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9798350345346

Unmanned aerial vehicles (UAVs) have mobility in harsh environments and the flexibility to modify their flight altitude to acquire information in an adaptive manner, so these vehicles can be leveraged to solve the informative path planning (IPP) problem. As most of the research on IPP currently are on static targets and terrain detection tasks, in this paper, we focus on the search for dynamic targets on the sea surface, and study the issue of collaborative search with multiple granularities using a swarm of UAVs in marine environments. To capture the objects of interest (OOI), we design a binary classifier for UAVs to sense OOI based on a depth perception model of cameras. Afterwards, we propose a collaborative search scheme using the multi-agent deep deterministic policy gradient (MADDPG) approach to enable a swarm of UAVs to simultaneously maintain formation coordination while adapting to digital pheromone strength. The experimental results show that the proposed scheme can effectively detect targets on the sea surface in a multi-granular collaborative manner. © 2023 IEEE.

关键词： Motion planning

来源：评论

学校读者我要写书评

暂无评论

A Social Distancing Framework Based on GPS and Bluetooth Empowered by Feature-based Machine Learning Algorithm

引用

IEIE Transactions on Smart Processing and Computing 2025年第1期14卷 118-127页

作者： Khan, Muhammad Adnan Rehman, Abdur Abbas, Sagheer Ali, Muhammad Nadeem Kim, Byung-Seo Department of Software Faculty of Artificial Intelligence & Software Gachon University Seongnam13120 Korea Republic of School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Science Prince Mohammad Bin Fahd University Al Khobar Saudi Arabia Department of Software and Communications Engineering Hongik University Sejong Campus Korea Republic of

The current COVID-19 epidemic is responsible for causing a catastrophe on a global scale due to its risky spread. The community’s insecurity is growing as a result of a lack of appropriate remedial measures and immunization against the disease. In this case, social distancing is thought to be an effective barrier against the spread of the contagion virus as the risk of virus transmission can be reduced by avoiding direct contact with people. Thus, the goal of this research is to develop and improve an AI (Artificial Intelligence) system architecture for social distance monitoring. The framework could also use the GPS (Global Positioning System) to recognize human separation through cell phones. The transition learning framework is also applied to improve the consistency of the existing system. In this manner, the detection system uses a pre-trained technique that takes a Bluetooth dataset and location-sharing dataset to link to an additional level. In an attempt to approximate social distancing breaches among people, we used Bluetooth technology along with GPS distance estimation and set a threshold. To predict if the distance value exceeds the required social distance standard, a violation threshold is calculated and then it sends an alarm to every individual who is not maintaining social distancing. In response, the individual who breaks the social distance limit is also monitored using a detection approach. © 2025 Institute of Electronics Engineers of Korea. All rights reserved.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Compression Algorithm Selection for Column-Oriented Databases Based on Stacking 11

Compression Algorithm Selection for Column-Oriented Database...

引用

11th Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2023

作者： Tian, Fuwen Xu, Meng College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9798350333664

Column-oriented databases have emerged as effective solutions for handling massive amounts of data, and data compression plays a crucial role. Attribute columns are divided into blocks and stored in separate files, and records within the same attribute column often exhibit high similarity, providing favorable conditions for data compression. Researchers have proposed numerous compression algorithms, but these algorithms can only achieve optimal compression results when applied to data with specific features. Therefore, it is crucial to select the compression algorithm that performs best based on the features of the data. We propose a novel method for selecting compression algorithms using Stacking, a technique that effectively enhances the prediction ability of classification models in machine learning. Our experimental results demonstrate that the stacked model achieves an accuracy of 92.9%, surpassing other models in terms of F1-Score and Kappa coefficient. Furthermore, our compression algorithm selection method outperforms other existing methods in terms of compression performance. © 2023 IEEE.

关键词： classification model column-oriented database data compression Stacking

来源：评论

学校读者我要写书评

暂无评论

Cooperative and Dynamic UAV-Based Mobile Sink Deployment using Deep Reinforcement Learning 11

Cooperative and Dynamic UAV-Based Mobile Sink Deployment usi...

引用

11th International Conference on Advanced Cloud and Big Data, CBD 2023

作者： Shao, Mingyue Luo, Hanjiang Ruby, Rukhsana Dong, Pengyan Tao, Hang Shandong University of Science and Technology College of Computer Science and Engineering Qingdao China Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9798350345346

In maritime emergency response operations, autonomous underwater vehicles (AUVs) can perform underwater search and transmit emergency data in real-time. To collect the sensed data from AUVs across the water-air interface, the traditional method is to deploy surface relays. However, the deployment of surface relays has some disadvantages, such as poor and imbalanced communication coverage of real-time connections with AUVs. Since the deployment of static relays may limit the dynamic search area of AUVs, whereas the deployment of free surface drifting relays may disrupt the connections while the relays are drifting away with the ocean current. To address these challenges, in this paper, we propose a novel method by leveraging the flexibility of multiple unmanned aerial vehicles (UAVs) equipped with underwater hydrophones and design a dynamic deployment scheme based on a multi-agent deep deterministic policy gradient (MADDPG) approach. By this scheme, multiple UAVs are able to deploy in a cooperative manner to deal with the coverage imbalance issue and provide maximum coverage service. The numerical simulations demonstrate that the proposed scheme is effective in terms of communication coverage, fairness, and energy consumption. © 2023 IEEE.

关键词： Autonomous underwater vehicles

来源：评论

学校读者我要写书评

暂无评论

Learning Contention Window Selection in Age of Information-Oriented IEEE 802.11 Networks 11

Learning Contention Window Selection in Age of Information-O...

引用

2023 IEEE 11th International Conference on Information, Communication and Networks, ICICN 2023

作者： Li, Jinhua Jian, Feng Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9798350314014

We study the problem of selecting the contention window (CW) for age of information (AoI)-oriented IEEE 802.11 networks using deep reinforcement learning (DRL) techniques. AoI quantifies information freshness and is defined as the time elapsed since the generation of the latest received information update. As the number of stations increases, conventional CW selection methods defined in IEEE 802.11 standards, such as binary exponential backoff (BEB), fail to achieve a low network-wide average AoI. Therefore, we leverage DRL and propose a CW control algorithm based on deep deterministic policy gradient (DDPG) that can dynamically learn appropriate CW values under different network conditions. Simulation results demonstrate that our proposed DRL-based approach reduces the average AoI of the system by about 57.6% in a congested network with 50 stations compared to the conventional BEB algorithm. Furthermore, compared to the algorithm using throughput as the DRL reward function, our AoI-reward-function approach achieves a 20.4% improvement in information freshness, indicating the key difference between AoI and throughput. © 2023 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Adaptive Lazily Aggregation Based on Error Accumulation 4

Adaptive Lazily Aggregation Based on Error Accumulation

引用

4th International Conference on Electronic Communication and Artificial Intelligence, ICECAI 2023

作者： Chen, Xiaofeng Liu, Gang Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9798350314496

Federated Learning (FL) enables multiple clients to collaboratively train models without exposing their local data. FL is an effective approach to utilizing localized data while preserving clients' data privacy, but it also brings significant communication overhead. To reduce communication overhead of FL, this paper proposes the Adaptive Lazily Aggregation based on Error Accumulation (EA-ALA) algorithm. It uses adaptive constraints to determine whether a client can skip a communication round with the server so as to diminish communication cost. It also adopts error accumulation to improve model accuracy. The experimental results on CIFAR10 and Fashion-MNIST datasets show that compared to vanilla FL, EA-ALA consumes only 52% and 61% of communication rounds to achieve higher model accuracy. © 2023 IEEE.

关键词： Errors

来源：评论

学校读者我要写书评

暂无评论

Deep Hierarchy Quantization Compression algorithm based on Dynamic Sampling 4

Deep Hierarchy Quantization Compression algorithm based on D...

引用

4th International Conference on Electronic Communication and Artificial Intelligence, ICECAI 2023

作者： Jiang, Wan Liu, Gang Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9798350314496

Federated learning allows multiple parties to jointly train deep learning models without the need for any participants to reveal their private data to a centralized server. However, this form of privacy-preserving collaborative learning has resulted in significant communication costs during the training period. To address this problem, we propose the Deep Hierarchical Quantization Compression (DHQC) algorithm. DHQC reduces the communication cost by using gradient sparsification and multi-bit quantization, and improves the accuracy and convergence speed of the model by using a dynamic sampling strategy. Experimental results on the CIFARIO and CIFARIOO datasets demonstrate that compared with the uncompressed baseline, DHQC can achieve a compression rate of 128x, significantly reducing the communication costs. © 2023 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Dynamic Constrained Multi-Objective Evolutionary Optimization via Adaptive Two-Stage Archiving and Autoencoder Prediction 5

Dynamic Constrained Multi-Objective Evolutionary Optimizatio...

引用

5th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2023

作者： Wang, Qianhui Ye, Yulong Zhu, Qingling Liu, Songbai Lin, Qiuzhen College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9798350393521

Dynamic constrained multi-objective optimization problems (DCMOPs) are characterized by time-varying objectives and constraints, requiring optimization algorithms that can rapidly track the changing Pareto-Optimal Set (POS).A new dynamic constrained multi-objective evolutionary algorithm with adaptive two-stage archiving and autoencoder prediction is proposed in this paper, called ATAP to effectively solve DCMOPs. Specifically, ATAP designs a differential denoising autoencoder (DDA) prediction strategy, which applies a denoising autoencoder to thoroughly analyze change trends of historical population and predict some initial solutions in the new environment. Subsequently, to promote greater diversity within the initial population, a differential rule is implemented, effectively addressing the potential scarcity of diversity caused by constraints. Moreover, ATAP introduces an adaptive two-stage archiving (ATA) constraint handling technique, which can dynamically adjust evolutionary stages based on the state of the population. This approach can adaptively determine preferences between objectives and constraints, achieving a better balance. In this way, ATA and DDA are well cooperated to efficiently solve DCMOPs with time-varying constraints and objectives. The experimental results demonstrate that the proposed ATAP is effective and has some advantages over three competitive algorithms when solving the CEC2023 DCMOP benchmark. © 2023 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

An interval RSP-based ensemble model for big data analysis 29

An interval RSP-based ensemble model for big data analysis

引用

29th International DMS Conference on Visualization and Visual Languages, DMSVIVA 2023

作者： Cai, Wenzhu Ao, GengYuan Lin, YiGang Li, Mark Junjie College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)1891706578

Ensemble learning for big data has been successful in machine learning and has great advantages over other learning methods. The ensemble model based on Random Sample Partition (RSP) is a prominent method of it. Although the RSP data blocks have the consistent probability distribution function as the whole data, there is some uncertainty in prediction results due to the non-overlapping data between blocks. In this paper, we propose a novel interval ensemble model based on RSP named Inr-RSP, which maps prediction results to interval-valued data by interval modeling and then uses the IAA aggregation method to convert the interval-valued data into fuzzy sets to get a more accurate and stable final result. The experimental classification results from four real datasets also show that the performance of this model is better than that of the traditional RSP ensemble model. And the IAA method usage has a stronger ability to capture uncertainty in prediction than the common majority voting method. Copyright © 2023 by KSI Research Inc. and Knowledge Systems Institute, USA.

关键词： Learning systems

来源：评论

学校读者我要写书评

暂无评论

A Neocognitron Based on Multi-objective Optimization for Few-Shot Learning 1

引用

6th EAI International Conference on Robotics and Networks, EAI ROSENET 2022

作者： Pan, Hao Guo, Guiyu Shi, Daming College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9783031338267

ISBN: (纸本)9783031338250

In recent years, with the continuous development of deep learning, more and more network models have been proposed to solve practical problems. However, most models often need a large number of labeled samples to train the network to achieve better recognition results. Because a large number of samples need to be labeled, it is labor-intensive and prone to human error labels. For this reason, a large number of researchers have created the concept of few-shot learning, which entails that the model acquires new information from a limited amount of labeled samples in order to get improved recognition outcomes. In this chapter, we learn from the structure of the visual cortex of the human brain and propose new shape templates to improve the traditional neural network. First, we extract the global characteristics of the image to form a 0-1 mask. As the network hierarchy increases and the field of perception expands, the mask is cut to form shape templates. Then evolutionary computation selects some excellent shape templates, which provide a non-linear constraint for the convolution layer. Moreover, the convolution layer introduces inhibition signals for training, and a new function is proposed to make it more in line with the biomimetic principles of the human visual cortex. The results show that the proposed method can improve the accuracy of few-shot learning. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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：