2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)最新文献_第6页

FPGA Design and Implementation of ECG Classification Neural Network 心电分类神经网络的FPGA设计与实现

2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201313

Tiantai Lu, Bowen Zhao, M. Xie, Zhifeng Ma

{"title":"FPGA Design and Implementation of ECG Classification Neural Network","authors":"Tiantai Lu, Bowen Zhao, M. Xie, Zhifeng Ma","doi":"10.1109/CCAI57533.2023.10201313","DOIUrl":"https://doi.org/10.1109/CCAI57533.2023.10201313","url":null,"abstract":"The multi-label classification algorithm of electrocardiogram can be applied in clinical diagnosis as an aid. By porting the algorithm to intelligent terminal devices, it can monitor the health of patients in real-time and provide disease warnings, allowing for the timely detection of potential cardiovascular diseases in users. In this paper, a lightweight multi-scale attention network is designed, and a multi-channel parallel accelerator based on output data reuse is developed specifically for this network. The accelerator adopts a deep pipeline parallel architecture, which can highly reuse data in time and space, making it suitable for deploying on hardware platforms with limited resources. The accelerator designed in this paper is deployed on Xilinx’s ZYNQ-7100 hardware platform, achieving a throughput of 116.7 GOPs with a power consumption of 6. 67W, and has a hardware resource utilization rate of 0.33 GOPS/DSP and 2.85 GOPS/kLUT. Compared with general CPUs/GPUs, this accelerator has greater advantages in terms of hardware utilization efficiency and energy consumption, which meets the requirements of low-power and high-performance for intelligent terminal devices.","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132787694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Federated Learning Empowered Resource Allocation in UAV-Assisted Edge Intelligent Systems 无人机辅助边缘智能系统中的联邦学习授权资源分配

2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201325

Bintao Hu, Matilda Isaac, Olukunle Mobolaji Akinola, H. Hafizh, Wenzhang Zhang

{"title":"Federated Learning Empowered Resource Allocation in UAV-Assisted Edge Intelligent Systems","authors":"Bintao Hu, Matilda Isaac, Olukunle Mobolaji Akinola, H. Hafizh, Wenzhang Zhang","doi":"10.1109/CCAI57533.2023.10201325","DOIUrl":"https://doi.org/10.1109/CCAI57533.2023.10201325","url":null,"abstract":"Mobile edge computing (MEC) has been considered a promising advanced technology to support delay-sensitive tasks of user equipment (UE) in the internet of things (IoT) systems, it is necessary to allow multiple UEs to offload their computationally intensive tasks to a flexible edge computing server, such as an unmanned aerial vehicle (UAV)-assisted edge computing server. However, most existing works mainly focused on minimising energy consumption under the transmission and/or processing delay constraints while ignoring privacy-preserving, which will be challenging when dealing with large volumes of raw data. In this paper, we consider a federated learning (FL) empowered UAV-assisted edge intelligent system to minimise the maximum utility cost (which indicates the relationship between latency and energy consumption) to the selected UE for task processing. We propose to jointly optimise the FL task offloading decisions among all UEs and the communication resource allocation under each epoch. This is achieved by devising a federated learning-based edge intelligence offloading decision optimisation algorithm (FEOA). Simulation results show that our proposed schemes outperform the benchmarks in terms of the maximum cost efficiency among all UEs.","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"169 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133679628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Chest X-ray Lesion Detection Based on Improved YOLOv7 基于改进YOLOv7的胸部x线病变检测

2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201327

Fuyang Jia, Chengzhe Xu

{"title":"Chest X-ray Lesion Detection Based on Improved YOLOv7","authors":"Fuyang Jia, Chengzhe Xu","doi":"10.1109/CCAI57533.2023.10201327","DOIUrl":"https://doi.org/10.1109/CCAI57533.2023.10201327","url":null,"abstract":"Chest x-ray examination is one of the important methods of clinical examination. Due to the complex structure of the chest, irregular lesions and other factors, it is difficult to clearly display the features, and the performance of chest X-ray lesion detection is limited. Therefore, this paper takes the characteristics of chest x-ray images as the starting point. According to the characteristics of chest x-ray lesions, the YOLOv7 algorithm has been improved. By introducing the MVB (MobileViT Block) module into the backbone feature extraction network, the correlation of multiple locations can be correlated, more global and accurate information can be extracted, and the irregular shape of the lesion can be effectively processed. At the same time, in view of the complex background noise of the chest radiograph image, the GAM (Global Attention Mechanism) is introduced in the feature pyramid fusion stage. This method can increase the importance of object focus and suppress the interference of background noise. Finally, through comparative experiments, this paper found that the improved chest x-ray lesion detection algorithm of mAP@.5 reached 0.62, and compared with the benchmark model YOLOv7 algorithm, the detection rate of the improved algorithm increased by 2.6%. In addition, the ablation experimental results show that the improvements made in this paper can effectively alleviate the problem in chest X-ray images.","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134444564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SAKP: A Korean Sentiment Analysis Model via Knowledge Base and Prompt Tuning 基于知识库和提示调优的韩文情感分析模型

2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201257

Haiqiang Wen, Zhenguo Zhang

{"title":"SAKP: A Korean Sentiment Analysis Model via Knowledge Base and Prompt Tuning","authors":"Haiqiang Wen, Zhenguo Zhang","doi":"10.1109/CCAI57533.2023.10201257","DOIUrl":"https://doi.org/10.1109/CCAI57533.2023.10201257","url":null,"abstract":"With the help of pre-trained language models, tasks such as sentiment analysis and text classification have achieved good results. With the advent of prompt tuning, especially previous studies have shown that in the case of few data, the prompt tuning method has significant advantages over the general tuning method of additional classifiers. At present, there are relatively few studies on sentiment analysis of Korean Chinese texts.This paper proposes a low resource sentiment classification method based on pre-trained language models (PLMs) combined with prompt tuning. In this work, we chose to use the pre-trained language model KLUE and elaborated a Korean prompt template with an expanded knowledge base and filtering in the verbalizer section. We focus on collecting external knowledge and integrating it into the utterance to form a prompt tuning of knowledge to improve and stabilize the prompt tuning. Specifically, we use the K-means clustering algorithm to construct the label wordspace of the external knowledge base (kb) extended language, and use PLM itself to refine the extended labeled wordspace before using the extended labeled wordspace for prediction. A large number of experiments on the few-shot emotion classification task prove the effectiveness of knowledge prompt tuning.","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129831286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Study on Optimization of Logistics Distribution Path of Rural E-commerce Enterprises 农村电子商务企业物流配送路径优化研究

2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201249

Xiaojun Deng, Xiang Li

引用次数: 0

Pure Exploration of Continuum-Armed Bandits under Concavity and Quadratic Growth Conditions 凹型和二次增长条件下连续武装土匪的纯粹探索

2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201299

Xiaotian Yu

{"title":"Pure Exploration of Continuum-Armed Bandits under Concavity and Quadratic Growth Conditions","authors":"Xiaotian Yu","doi":"10.1109/CCAI57533.2023.10201299","DOIUrl":"https://doi.org/10.1109/CCAI57533.2023.10201299","url":null,"abstract":"The traditional setting for pure exploration of multi-armed bandits is to identify an optimal arm in a decision set, which contains a finite number of stochastic slot machines. The finite-arm setting restricts classic bandit algorithms, because the decision set for optimal selection can be continuous and infinite in many practical applications, e.g., determining the optimal parameter in communication networks. In this paper, to generalize bandits into wider real scenarios, we focus on the problem of pure exploration of Continuum-Armed Bandits (CAB), where the decision set is a compact and continuous set. Compared to the traditional setting of pure exploration, identifying the optimal arm in CAB raises new challenges, of which the most notorious one is the infinite number of arms. By fully taking advantage of the structure information of payoffs, we successfully solve the challenges. In particular, we derive an upper bound of sample complexity for pure exploration of CAB with concave structures via gradient methodology. More importantly, we develop a warm-restart algorithm to solve the problem where a quadratic growth condition is further satisfied, and derive an improved upper bound of sample complexity. Finally, we conduct experiments with real-world oracles to demonstrate the superiority of our warm-restart algorithm.","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129629632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on Seismic Data Denoising Based on Dual Channel Residual Attention Network 基于双通道残差注意网络的地震数据去噪研究

2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201253

Yuxiang Liu, Yinghua Zhou, Xiaodan Liu

{"title":"Research on Seismic Data Denoising Based on Dual Channel Residual Attention Network","authors":"Yuxiang Liu, Yinghua Zhou, Xiaodan Liu","doi":"10.1109/CCAI57533.2023.10201253","DOIUrl":"https://doi.org/10.1109/CCAI57533.2023.10201253","url":null,"abstract":"In recent years, seismic data denoising has attracted more and more scholars' attention and research, and the suppression of random noise is the key to improving the signal-to-noise ratio of seismic data. Aiming at the problem that traditional denoising methods are difficult to effectively remove a large amount of random noise and retain effective signals, we propose a neural network model based on dual channel residual attention network (DCRANet). Specifically, the model consists of a residual attention block (RAB), a dilated convolution sparse block (DCSB) and a feature enhancement block (FEB). The residual blocks in RAB can avoid some problems such as gradient vanishing and gradient exploding when the network is too deep, and the use of attention mechanism can guide the network to effectively extract complex noise information. The DCSB recovers the useful details from complex noise information by expanding the receptive field, fully acquiring important structural information and edge features of seismic data. The FEB integrates the noise features extracted by RAB and DCSB, it uses convolutional layers to extract the noise information of seismic data, and finally reconstructs clean seismic data image by the residual learning strategy. Compared with NL-Bayes, BM3D, DnCNN, CBDNet and DudeNet, DCRANet effectively suppresses random noise while retaining more local details and obtains a higher average peak signal-to-noise ratio (PSNR) and average structural similarity (SSIM).","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125528970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Early Warning and Screening of Elderly Cognitive Impairment Based on Machine Learning Algorithm 基于机器学习算法的老年认知障碍早期预警与筛查

2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201263

Qinyang Chen, Wen Hou, Xinyue Wang, Weiying Zheng, Muzhou Hou, Hui Zeng, Lianglun Cheng

{"title":"Early Warning and Screening of Elderly Cognitive Impairment Based on Machine Learning Algorithm","authors":"Qinyang Chen, Wen Hou, Xinyue Wang, Weiying Zheng, Muzhou Hou, Hui Zeng, Lianglun Cheng","doi":"10.1109/CCAI57533.2023.10201263","DOIUrl":"https://doi.org/10.1109/CCAI57533.2023.10201263","url":null,"abstract":"In order to improve the classification accuracy in the early warning and screening process of elderly cognitive impairment, this paper constructs a screening system for elderly cognitive impairment based on the survey data of community elderly residents in Changsha. The cognitive level of all samples was divided into normal, mild cognitive impairment (MCI) and cognitive disorder. Firstly, the correlation between all features and sample categories is described by mutual information, and the features that have no significant impact on sample classification are eliminated. Secondly, support vector machine (SVM) and random forest were used for sample classification. When determining the hyperparameters of the model, the learning curve based on generalization error is used for parameter combination optimization, and a variety of evaluation indexes are used to evaluate the performance of the model. Experimental results show that SVM has more accurate classification ability than random forest, while random forest is more “conservative” and tends to identify normal samples as abnormal ones, which can reduce the risk of loss of potential patients and is more suitable for the situation where screening work needs to find potential patients as much as possible.","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122083362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Compare with the Traditional Heterogeneous Solution: Accelerate Neural Network Algorithm through Heterogeneous Integrated CPU+NPU Chip on Server 与传统异构解决方案的比较:通过服务器上异构集成CPU+NPU芯片加速神经网络算法

2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201248

Xiancheng Lin, Xiangyu Zhou, Rongkai Liu, Xiang Gao

{"title":"Compare with the Traditional Heterogeneous Solution: Accelerate Neural Network Algorithm through Heterogeneous Integrated CPU+NPU Chip on Server","authors":"Xiancheng Lin, Xiangyu Zhou, Rongkai Liu, Xiang Gao","doi":"10.1109/CCAI57533.2023.10201248","DOIUrl":"https://doi.org/10.1109/CCAI57533.2023.10201248","url":null,"abstract":"The increasing popularity of artificial intelligence (AI) requires the ability to process intensive data and efficient heterogeneous computing power. As a result, a heterogeneous integration scheme involving both central processing units (CPUs) and neural processing units (NPUs) has become increasingly prevalent in various edge terminals, such as mobile phones. Compared with traditional separated heterogeneous solutions, the integration scheme can effectively reduce the distance and number of data transmissions, thereby accelerating deep neural network (DNN) models and improving energy efficiency. Due to the low power requirements of cloud computing, heterogeneous integration solutions are beginning to be used in the design of processor architectures for servers. The TF16110 integrates NPUs into server CPUs, creating an efficient parallel computing solution for servers that lack GPUs or other AI acceleration devices. In this paper, we evaluate and analyze commonly used DNN models. Compared with NVIDIA’s TX2 GPU, the heterogeneous integrated CPU+NPU design can provide similar computational power and achieve 5x higher energy efficiency and 10x cost-effectiveness under the premise of ensuring accuracy","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122156539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Global-Best Brain Storm Optimization Algorithm Based on Discussion Mechanism and Difference Step 基于讨论机制和差分步的全局最优头脑风暴优化算法

2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201321

Yanchi Zhao, Jia-Ping Cheng, Jing Cai

引用次数: 0