检索结果-内蒙古大学图书馆

Edge Intelligence Based Garbage Classification Detection Method 1

3rd EAI International Conference on Edge Computing and IoT, EAI ICECI 2022

作者： Zhu, Ruijia Liu, Yiwen Gao, Yanxia Shi, Yuanquan Peng, Xiaoning School of Computer and Artificial Intelligence Huaihua University Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua418000 China

ISBN: (数字)9783031289903

ISBN: (纸本)9783031289897

To address the problem that the classification and cleaning of garbage in city streets is always ineffective nowadays, the paper proposes a garbage detection method based on edge intelligence. The edge intelligence not only reduces the computational load of the cloud and speeds up the data transmission, but also greatly reduces the data transmission cost. First, images of city streets are collected and uploaded to the edge device via mobile devices in various locations in the city. Then, the edge server is used to temporarily store the image information, and the PeleeNet model deployed on it is used to identify and classify various kinds of garbage, and then visualize the information of each street. Finally, the street garbage information is transmitted to the cloud, which provides a detailed picture of the city’s garbage situation and facilitates city management. In this paper, the PeleeNet model is compared with ResNet, DenseNet and MobileNet models. The results show that the edge devices equipped with PeleeNet model not only have the fastest computation speed and the highest accuracy, but also occupy the least memory. It is fully demonstrated that the method studied in the paper can be applied to the problem of litter detection in urban streets. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Smart city

来源：评论

学校读者我要写书评

暂无评论

A Self-decoupled Interpretable Prediction Framework for Highly-Variable Cloud Workloads 28th

A Self-decoupled Interpretable Prediction Framework for Hig...

引用

28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Wang, Bingchao Shi, Xiaoyu Shang, Mingsheng Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China Chongqing School University of Chinese Academy of Sciences Chongqing400714 China Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing400065 China

ISBN: (纸本)9783031306365

Cloud workloads prediction plays a crucial role in the various tasks of cloud computing, such as resource scheduling, performance optimization, cost management, etc. However, current time series prediction methods suffer instability and inefficiency issues when addressing cloud workloads, due to the high variability of workload patterns and the high fluctuation within a workload. To address these issues, we propose DeIP4CW, a Self-Decoupled Interpretable Prediction framework for highly-variable Cloud Workloads. It can accurately forecast future job arrival rates in a cloud environment. The core idea of DeIP4CW is to first introduce the periodic and residual states as hidden variables to decouple complicated dependencies in cloud workload signals. Then it adopts a deep expansion learning framework with the block structure to perform workload prediction layer by layer. Each block consists of some periodic modules and some compensation modules. The periodic module with a self-attention mechanism can effectively capture the global trend of cloud workload, while the compensation module is employed to compensate for the local volatility information. Moreover, our two customized modules also have interpretable abilities, such as attributing the predictions to either global trends or local compensation. We conduct extensive experiments on the real-world cloud workload traces to evaluate the effectiveness of the proposed DeIP4CW. The experimental results demonstrate the DeIP4CW achieves significant improvements over the best baseline in most cases, and the error reduction can even reach up to 20.66%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cloud computing

来源：评论

学校读者我要写书评

暂无评论

Highly Accurate Dynamic Gesture Recognition Method Based on Edge Intelligence 1

引用

3rd EAI International Conference on Edge Computing and IoT, EAI ICECI 2022

作者： Changkai, Lu Yiwen, Liu Yanxia, Gao Yuanquan, Shi Xiaoning, Peng School of Computer and Artificial Intelligence Huaihua University Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua418000 China

ISBN: (数字)9783031289903

ISBN: (纸本)9783031289897

In recent years, gestures have been widely used in many fields. For example, human-computer interaction, virtual reality, gesture translation, etc. The reason for its rapid development is due to the emergence of deep learning and artificial intelligence under today’s society. Due to dynamic gestural interactions, such large intelligent models are often characterized by many parameters, large sample size, frequent parameter updates, and high communication volume. Based on this feature, we propose a cloud-based edge design architecture approach for gesture recognition based on an improved YOLOv5 network model optimization by changing different gestures, background interference, which uses a 21-layer model for the neural network. With the use of edge intelligence, the computational accuracy can be improved by 10.6% over the traditional YOLOv5 with a MAP value of 93.3%. The final recall rate is also improved by 3.6%. The parameter model is only 43.6% of the original one. This shows the practicality and operability of using edge computing as a technique in gesture recognition, as well as the small improvement cost and obvious effect. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Gesture recognition

来源：评论

学校读者我要写书评

暂无评论

Prediction of remaining useful life of lithium batteries based on Decayable-LSTM

Prediction of remaining useful life of lithium batteries bas...

引用

Chinese Control and Decision Conference, CCDC

作者： Penghua Li Dailin Gao Key Laboratory of Intelligent Computing for Big Data College of Automation Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

data-driven techniques have been extensively employed in practical applications involving lithium-ion batteries. However, the accuracy of these methods heavily relies on the quality and quantity of the collected data. The presence of missing values, resulting from external factors, poses a challenge in effectively leveraging the collected data. To address this issue, this paper proposes a novel approach that utilizes long short-term memory (LSTM) to extract valuable features from randomly missing data. Additionally, an interactive network organized in a tree structure is employed for predicting the remaining useful life (RUL). Experimental results on the NASA dataset demonstrate the superiority of our method, achieving a lower root mean square error(RMSE) of 0.01265 ± 0.001 for RUL, surpassing other data-driven methods.

关键词： Lithium-ion batteries Computational modeling NASA Predictive models Feature extraction Attenuation Lithium batteries

来源：评论

学校读者我要写书评

暂无评论

Delving Into Important Samples of Semi-Supervised Old Photo Restoration: A New dataset and Method

引用

IEEE TRANSACTIONS ON MULTIMEDIA 2024年 26卷 9866-9879页

作者： Cai, Weiwei Zhang, Huaidong Xu, Xuemiao Xu, Chenshu Zhang, Kun He, Shengfeng South China Univ Technol Sch Comp Sci & Engn Guangzhou 510641 Peoples R China South China Univ Technol Sch Future Technol Guangzhou 510641 Peoples R China Minist Educ State Key Lab Subtrop Bldg Sci Key Lab Big Data & Intelligent Robot Guangdong Prov Key Lab Computat Intelligence & Cyb Guangzhou 510515 Peoples R China Singapore Management Univ Sch Comp & Informat Syst Singapore 188065 Singapore Carnegie Mellon Univ Philosophy Dept Pittsburgh PA 15251 USA

The degradation of printed photographs due to inadequate preservation is a major problem that can be addressed through deep learning-based restoration methods. However, these methods are often limited by their reliance on annotated data, making them less effective for new domains with limited training samples. In this paper, we propose a semi-supervised old photo restoration network that employs a continuous important sample mining strategy. Specifically, we explore the learning potential of limited data from three aspects: correcting imbalanced data distribution, assigning significant pseudo labels, and learning from unlabeled data. First, we coordinate a random mask augmented strategy with the Double-consistency Alignment method to address the unbalanced damaged category (scratched damage is more prevalent than other artifact types). Second, we develop a novel Perceptual-aware Pseudo-label Propagation method that selects initial recovered results as reliable pseudo-labels to continuously expand the sample pool. Lastly, we propose a Damage-augmented Contrastive Learning method that constructs positive, anchor, and negative samples within a semi-supervised framework to mine correlations of unlabeled data more effectively. To evaluate our approach, we introduce the Old Photo Detection dataset (OPDD) and the Old Photo Restoration dataset (OPRD), both of which consist of 563 (6,179 augmented) photo pairs recovered by professional artists. Our extensive experiments show that our approach significantly outperforms existing methods. Furthermore, we demonstrate the effectiveness of our approach by training an external old photographic plate restoration network using the deuterogenic old photographic film dataset and obtaining promising results.

关键词： Image restoration Training Task analysis Training data Semisupervised learning Correlation Reliability Old photo restoration semi-supervised learning contrastive learning

来源：评论

学校读者我要写书评

暂无评论

WISE: Rethinking the Knowledge Memory for Lifelong Model Editing of Large Language Models

arXiv

引用

arXiv 2024年

作者： Wang, Peng Li, Zexi Zhang, Ningyu Xu, Ziwen Yao, Yunzhi Jiang, Yong Xie, Pengjun Huang, Fei Chen, Huajun Zhejiang University China Alibaba Group China Zhejiang Key Laboratory of Big Data Intelligent Computing China

Large language models (LLMs) need knowledge updates to meet the ever-growing world facts and correct the hallucinated responses, facilitating the methods of lifelong model editing. Where the updated knowledge resides in memories is a fundamental question for model editing. In this paper, we find that editing either long-term memory (direct model parameters) or working memory (nonparametric knowledge of neural network activations/representations by retrieval) will result in an impossible triangle—reliability, generalization, and locality can not be realized together in the lifelong editing settings. For long-term memory, directly editing the parameters will cause conflicts with irrelevant pretrained knowledge or previous edits (poor reliability and locality). For working memory, retrieval-based activations can hardly make the model understand the edits and generalize (poor generalization). Therefore, we propose WISE to bridge the gap between memories. In WISE, we design a dual parametric memory scheme, which consists of the main memory for the pretrained knowledge and a side memory for the edited knowledge. We only edit the knowledge in the side memory and train a router to decide which memory to go through when given a query. For continual editing, we devise a knowledge-sharding mechanism where different sets of edits reside in distinct subspaces of parameters and are subsequently merged into a shared memory without conflicts. Extensive experiments show that WISE can outperform previous model editing methods and overcome the impossible triangle under lifelong model editing of question answering, hallucination, and out-of-distribution settings across trending LLM architectures, e.g., GPT, LLaMA, and Mistral. Copyright © 2024, The Authors. All rights reserved.

关键词： Memory architecture

来源：评论

学校读者我要写书评

暂无评论

Exploration and Practice of Course Homework Metaverse Based on Extended Reality Under Edge Computing 1

引用

3rd EAI International Conference on Edge Computing and IoT, EAI ICECI 2022

作者： Fu, Jinrong Liu, Yiwen Yan, Haobo Gao, Yanxia Xiao, Jianhua School of Computer and Artificial Intelligence Huaihua University Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua418000 China

ISBN: (数字)9783031289903

ISBN: (纸本)9783031289897

The metaverse originated from science fiction at first, but it gradually came into reality with the continuous power of technology. Aiming at the problems of course work in Chinese universities, such as its function is weakened or suppressed, its form is too abstract, and it lacks elaborate design, this paper proposes a design method of course work meta-universe based on extended reality under edge computing. XR technology is used to realize the interactive immersion of the coursework meta-universe, in order to improve students’ learning enthusiasm and ability to solve abstract problems. Based on the international perspective, this paper systematically combs and analyzes the integration architecture, application scenarios and future trends of the meta-universe education application based on Edge computing (MEC). Four key processes are described: teacher-student interaction, data collection, data feedback and comprehensive evaluation. In order to promote the application of the metaverse in the field of education to provide mirror. The validity of the method is illustrated by the data. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Edge computing

来源：评论

学校读者我要写书评

暂无评论

Sharing, Teaching and Aligning: Knowledgeable Transfer Learning for Cross-Lingual Machine Reading Comprehension

Sharing, Teaching and Aligning: Knowledgeable Transfer Learn...

引用

Conference on Empirical Methods in Natural Language Processing (EMNLP)

作者： Cao, Tingfeng Wang, Chengyu Tan, Chuanqi Huang, Jun Zhu, Jinhui South China Univ Technol Sch Software Engn Guangzhou Peoples R China Alibaba Grp Hangzhou Peoples R China South China Univ Technol Minist Educ Key Lab Big Data & Intelligent Robot Guangzhou Peoples R China Alibaba Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9798891760615

In cross-lingual language understanding, machine translation is often utilized to enhance the transferability of models across languages, either by translating the training data from the source language to the target, or from the target to the source to aid inference. However, in cross-lingual machine reading comprehension (MRC), it is difficult to perform a deep level of assistance to enhance cross-lingual transfer because of the variation of answer span positions in different languages. In this paper, we propose X-STA, a new approach for cross-lingual MRC. Specifically, we leverage an attentive teacher to subtly transfer the answer spans of the source language to the answer output space of the target. A Gradient-Disentangled Knowledge Sharing technique is proposed as an improved cross-attention block. In addition, we force the model to learn semantic alignments from multiple granularities and calibrate the model outputs with teacher guidance to enhance cross-lingual transferability. Experiments on three multi-lingual MRC datasets show the effectiveness of our method, outperforming state-of-the-art approaches. (1)

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

A Unified Transformer Framework for Group-Based Segmentation: Co-Segmentation, Co-Saliency Detection and Video Salient Object Detection

引用

IEEE TRANSACTIONS ON MULTIMEDIA 2024年 26卷 313-325页

作者： Su, Yukun Deng, Jingliang Sun, Ruizhou Lin, Guosheng Su, Hanjing Wu, Qingyao South China Univ Technol Sch Software Engn Key Lab Big Data & Intelligent Robot Minist Educ Guangzhou 510006 Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore Tencent Shenzhen 518000 Peoples R China South China Univ Sch Software Engn Pazhou Lab Guangzhou 510006 Peoples R China Peng Cheng Lab Shenzhen 518066 Peoples R China

Humans tend to mine objects by learning from a group of images or several frames of video since we live in a dynamic world. In the computer vision area, many researchers focus on co-segmentation (CoS), co-saliency detection (CoSD) and video salient object detection (VSOD) to discover the co-occurrent objects. However, previous approaches design different networks for these similar tasks separately, and they are difficult to apply to each other. Besides, they fail to take full advantage of the cues among inter- and intra-feature within a group of images. In this paper, we introduce a unified framework to tackle these issues from a unified view, term as UFGS (Unified Framework for Group-based Segmentation). Specifically, we first introduce a transformer block, which views the image feature as a patch token and then captures their long-range dependencies through the self-attention mechanism. This can help the network to excavate the patch-structured similarities among the relevant objects. Furthermore, we propose an intra-MLP learning module to produce self-mask to enhance the network to avoid partial activation. Extensive experiments on four CoS benchmarks (PASCAL, iCoseg Internet and MSRC), three CoSD benchmarks (Cosal2015, CoSOD3k, and CocA) and five VSOD benchmarks (DAVIS(16), FBMS, ViSal, SegV2, and DAVSOD) show that our method outperforms other state-of-the-arts on three different tasks in both accuracy and speed by using the same network architecture, which can reach 140 FPS in real-time.

关键词： Co-object long-range dependency transformer activation

来源：评论

学校读者我要写书评

暂无评论

Effect of energy back transfer from Er3+ to Yb3+ ions on the upconversion luminescence of Er:NaYb(MoO4)2 and Yb,Er:NaBi(MoO4)2

引用

Frontiers of Optoelectronics 2025年第1期18卷 12-12页

作者： Wang, Miaomiao Zhang, Mengyu Ding, Shoujun Hu, Haitang Zhang, Chuancheng Zou, Yong School of Microelectronics and Data Science Anhui University of Technology Maanshan243002 China Anhui Provincial Joint Key Laboratory of Disciplines for Industrial Big Data Analysis and Intelligent Decision Maanshan243002 China

Under the excitation of a 980 nm laser, the visible upconversion (UC) luminescence of Er3+ ions doped Yb3+ ions self-activated NaYb(MoO4)2 phosphor and crystal, as well as the Yb3+/Er3+ ions codoped NaBi(MoO4)2 crystal were investigated comprehensively. The results indicate that all three samples exhibit two significant green emission bands and a weak red emission band in the visible band corresponding to the transitions of 2H11/2/4S3/2 → 4I15/2 and 4F9/2 → 4I15/2 of Er3+ ions, respectively. Through the variable power density spectra of three different samples, the relationship between the energy back transfer (EBT) process of Yb3+-Er3+ ions and the power density point and Yb3+ ion concentration was investigated. The EBT process was observed in both the Er3+ ions doped Yb3+ ions self-activated NaYb(MoO4)2 phosphor and crystal, as confirmed by the luminescence image of the sample. At high power density, the Yb3+ ions self-activated sample exhibited yellow luminescence, with the crystal appearing later than the phosphor. In contrast, the NaBi(MoO4)2 crystal displayed bright green emission within the measured power density range. In addition, by monitoring the relative intensity change of Yb3+ emission in 5 at% Er3+:NaYb(MoO4)2 crystal, the generation of EBT process in self-activated samples at high power density is more directly explained. These experimental results provide a reliable basis for our comprehensive understanding of the EBT mechanism, and also provide a reliable direction for the final determination of the optimal excitation power density for optical temperature measurement. © The Author(s) 2025.

关键词： Temperature measurement

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：