2022 6th Asian Conference on Artificial Intelligence Technology (ACAIT)最新文献_第2页

RECC: A Relationship-Enhanced Content Caching Algorithm Using Deep Reinforcement Learning RECC:使用深度强化学习的关系增强内容缓存算法

2022 6th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2022-12-09 DOI: 10.1109/ACAIT56212.2022.10137967

Jiarui Ren, Haiyan Zhang, Xiaoping Zhou, Menghan Zhu

{"title":"RECC: A Relationship-Enhanced Content Caching Algorithm Using Deep Reinforcement Learning","authors":"Jiarui Ren, Haiyan Zhang, Xiaoping Zhou, Menghan Zhu","doi":"10.1109/ACAIT56212.2022.10137967","DOIUrl":"https://doi.org/10.1109/ACAIT56212.2022.10137967","url":null,"abstract":"Mobile edge caching (MEC) is a promising technology to alleviate traffic congestion in the network. Current studies explored deep reinforcement learning (DRL)-based MEC methods. These methods consider the dynamics of the request size to maximize the cache hit rate. However, they usually ignored the potential request relationships among contents. Two contents with a strong relationship are usually requested sequentially. Inspired by this assumption, this paper proposes a relationship-enhanced content caching algorithm using DRL, named RECC. Our RECC infers user preferences by mining the request relationships among contents. In this work, the relationships are modeled as request sequences, and the request features are learned by using graph embedding. These features will be used as input of state in our DRL-based algorithm. We utilize the Wolpertinger architecture to solve the limitation of large discrete action space. The simulation results indicate that our RECC outperformed the traditional cache policies and state-of-the-art DRL-based method in cache hit rate. Furthermore, the proposed RECC has advantages in long-term stability in the environment where content popularity changes dynamically, and also has a higher cache hit rate when handling the requests with number changes dynamically.","PeriodicalId":398228,"journal":{"name":"2022 6th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132704626","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

Determination of the Mechanical Origination of Wheel-Rail Rolling Noise Based on Spectrum Analysis 基于频谱分析的轮轨滚动噪声机械来源的确定

2022 6th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2022-12-09 DOI: 10.1109/ACAIT56212.2022.10137976

Qiushi Hao, Jia Ren

{"title":"Determination of the Mechanical Origination of Wheel-Rail Rolling Noise Based on Spectrum Analysis","authors":"Qiushi Hao, Jia Ren","doi":"10.1109/ACAIT56212.2022.10137976","DOIUrl":"https://doi.org/10.1109/ACAIT56212.2022.10137976","url":null,"abstract":"Acoustic emission technology has a great advantage over existing nondestructive technologies for real-time inspection, which will significantly improve the efficiency of wheel/rail defect detection. However, wheel-rail rolling noise impedes the application of acoustic emission technology in on-line operation, especially in high-speed or heavy-load condition. The key problem lies in that current researches haven’t developed adequate knowledge of the noise, making it difficult to gain the defect signal under the strong noise. To study mechanical originations of the noise and reveal its intrinsic properties, a spectral analysis method is proposed based on a fractal description of rough surfaces. Power spectra of the surface and those of the noise, as well as the relation of their fractal dimensions, are investigated. Then, under the instruction of spectral distributions of microscopic mechanical behaviors, the noise originations and influence of the vehicle speed are determined. It is found that the noise is generated based on the surface topography, while sliding friction, particle behavior, and abrasive wear are the main mechanical sources. The sliding friction dominates among the three behaviors. The speed promotes all the behaviors and then enhances the power level, while its effects on the sliding friction is relatively severer. The work offers a theoretical basis and mechanical explanation for the noise, which provides further guidance for the real-time detection of defect signals.","PeriodicalId":398228,"journal":{"name":"2022 6th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132738506","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

ASHN for Multi-Human Pose Estimation 多人体姿态估计的ASHN算法

2022 6th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2022-12-09 DOI: 10.1109/ACAIT56212.2022.10137930

Pan Gao, Zhuhua Hu

引用次数: 0

Association Estimation of English Education Level Using Artificial Neural Network Algorithm 基于人工神经网络算法的英语教育水平关联估计

2022 6th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2022-12-09 DOI: 10.1109/ACAIT56212.2022.10137851

Y. Huang

{"title":"Association Estimation of English Education Level Using Artificial Neural Network Algorithm","authors":"Y. Huang","doi":"10.1109/ACAIT56212.2022.10137851","DOIUrl":"https://doi.org/10.1109/ACAIT56212.2022.10137851","url":null,"abstract":"In order to improve the accuracy of English education level evaluation, this paper puts forward a design method of associated estimation model of English education level based on artificial neural network. Establish a multiattribute decision-making constraint parameter model for the correlation assessment of English education level, and analyze the multi-attribute decision-making and quantitative characteristics of the correlation assessment of English education level combined with the multi-dimensional explanatory variable and control variable parameter identification methods. Combined with the artificial neural network modeling method, the feature clustering analysis of the English education level is carried out; the adaptive learning and training method of the artificial neural network is used to establish the attribute fusion set and the semantic ontology feature distribution set of the multi-attribute decision-making for the correlation evaluation of the English education level; using the artificial neural network The network output layer fusion control method realizes the optimization of the multiattribute decision-making process. The simulation results show that the method has a good effect on the intelligent decisionmaking of the correlation evaluation of English education level, and improves the accuracy of the evaluation results of English education level.","PeriodicalId":398228,"journal":{"name":"2022 6th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115123098","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

Multi-Objective Optimization of Room Temperature Regulation of Building Phase Change Materials Based on Genetic Algorithm 基于遗传算法的建筑相变材料室温调节多目标优化

2022 6th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2022-12-09 DOI: 10.1109/ACAIT56212.2022.10137892

Siyu Wang, Dayong Dai

引用次数: 0

A Virtual Try-on Model with Enhanced Feature Representation Capability 一种增强特征表示能力的虚拟试戴模型

2022 6th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2022-12-09 DOI: 10.1109/ACAIT56212.2022.10137971

Hui Ma, Zhuhua Hu, Yan Zheng

引用次数: 0

Human-AI Co-Creation of Art Based on the Personalization of Collective Memory 基于集体记忆个性化的人-人工智能艺术协同创作

2022 6th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2022-12-09 DOI: 10.1109/ACAIT56212.2022.10137839

Zhuohao Wu, Yanni Li, Danwen Ji, Dingming Wu, M. Shidujaman, Yuan Zhang, Chenfan Zhang

引用次数: 0

Fine-Grained Complex Image Classification Method Based on Butterfly Images 基于蝴蝶图像的细粒度复杂图像分类方法

2022 6th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2022-12-09 DOI: 10.1109/ACAIT56212.2022.10137872

Yiping Rong, Han Su, Wenxin Zhang, Zhongyan Li

{"title":"Fine-Grained Complex Image Classification Method Based on Butterfly Images","authors":"Yiping Rong, Han Su, Wenxin Zhang, Zhongyan Li","doi":"10.1109/ACAIT56212.2022.10137872","DOIUrl":"https://doi.org/10.1109/ACAIT56212.2022.10137872","url":null,"abstract":"Fine-grained image classification is very difficult task and there is currently no effective machine learning method yet. Taking the butterfly images as an example, this paper comprehensively studies image classification methods for finegrained and complex images, focusing on improving the existing methods in the four aspects of image preprocessing, feature extraction, feature coding, and classifier design. An effective machine learning method for butterfly classification is established. In the aspect of image preprocessing, we firstly introduce the edge breakpoint connection method to make up for the defect that discontinuous edge can’t extract the target region effectively. In terms of feature extraction, nonseparable Shannon wavelet is used to combine with corner feature, blob feature, edge curvature feature and invariant moment feature selection to further improve the utilization of image information. In the aspect of feature encoding, most features in this paper are encoded by histogram, which increases the classification efficiency, and for histogram encoding features, an improved histogram intersection distance is proposed, which makes it more effective in KNN classifier. Finally, in the classifier design, the Bagging ensemble method is integrated into the parallel KNN classifier. Experiments show the effectiveness and robustness of the proposed method.","PeriodicalId":398228,"journal":{"name":"2022 6th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"275 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129174271","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

Performance Optimization in Energy Harvesting Cognitive Radio Networks a Shift Towards Metaheuristics 能量收集认知无线电网络的性能优化:向元启发式的转变

2022 6th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2022-12-09 DOI: 10.1109/ACAIT56212.2022.10137997

Shalley Bakshi, Surbhi Sharma, R. Khanna

引用次数: 0

The Use of Explainable Artificial Intelligence in Music—Take Professor Nick Bryan-Kinns’ “XAI+Music” Research as a Perspective 可解释人工智能在音乐中的应用——以Nick Bryan-Kinns教授的“XAI+Music”研究为视角

2022 6th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2022-12-09 DOI: 10.1109/ACAIT56212.2022.10137983

Meixia Li

引用次数: 0