Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition最新文献_第2页

Real-Time Calibration Method of Air Quality Data Based on AdaBoost Training Model 基于AdaBoost训练模型的空气质量数据实时校准方法

Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition Pub Date : 2022-11-17 DOI: 10.1145/3581807.3581882

Xuejing Jiang, Xun Sun, Qiuming Liu

{"title":"Real-Time Calibration Method of Air Quality Data Based on AdaBoost Training Model","authors":"Xuejing Jiang, Xun Sun, Qiuming Liu","doi":"10.1145/3581807.3581882","DOIUrl":"https://doi.org/10.1145/3581807.3581882","url":null,"abstract":"At present, a large number of cities are facing the situation of \"garbage besieged\", and the existing garbage disposal system can no longer meet the increasingly complex factors. With the development of a new generation of Internet of Things technology, integrating the knowledge and technology of related disciplines such as network and geographic information, it is possible to build a real-time monitoring platform for seepage and odor in landfills to complete gas monitoring. The author of the paper reviewed the related technologies of the Internet of Things, and proposed the design scheme of the online monitoring system for odor and seepage of the Maiyuan garbage dump in Nanchang City, selected 5 monitoring items, completed the data collection, and used the collected data to use Matlab and python software to carry out simulation analysis and prediction, and finally discuss the main factors and treatment measures of environmental pollution, provide theoretical guidance for relevant managers to improve the overall management decision-making level of urban domestic garbage dumps, and draw some practical conclusions.","PeriodicalId":292813,"journal":{"name":"Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126848474","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

Design and Simulation of A RF Front-end Circuit of Dual Channel Navigation Receiver 双通道导航接收机射频前端电路的设计与仿真

Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition Pub Date : 2022-11-17 DOI: 10.1145/3581807.3581905

Yu Zhang, Qiang Wu, Jie Liu

引用次数: 0

Simulation and Design of a 56Gbps Cross-backplane Transmission Channel 56Gbps跨背板传输信道的仿真与设计

Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition Pub Date : 2022-11-17 DOI: 10.1145/3581807.3581867

Kai Yao, Qiang Wu, Jinling Cui

{"title":"Simulation and Design of a 56Gbps Cross-backplane Transmission Channel","authors":"Kai Yao, Qiang Wu, Jinling Cui","doi":"10.1145/3581807.3581867","DOIUrl":"https://doi.org/10.1145/3581807.3581867","url":null,"abstract":"Over the past decade, new technologies and applications such as artificial intelligence, cloud services, and big data have led to an exponential increase in Internet-connected devices and data traffic. This puts forward higher requirements for bandwidth and stability of data transmission. In order to achieve the goal of device integration and miniaturization, the backplane structure is often used to realize the interconnect between board and card systems. The backplane is used as the basis for data exchange. However, the long-distance transmission across the backplane will cause serious losses and various signal integrity problems. In recent years, with the development of serial transmission, 56Gbps PAM4 modulation transmission has gradually shown transmission efficiency beyond 28Gbps NRZ. The change of modulation mode brings an inherent loss of 9.5dB, and PAM4 modulation has more stringent requirements on signal integrity. In this paper, ADS is used to model and simulate the cross-backplane long-distance transmission channel, and a set of high-speed transmission channel design scheme based on OIF CSI-56G-LR specification is established from the aspects of plate, laminates and holes.","PeriodicalId":292813,"journal":{"name":"Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121522834","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

GANExplainer: Explainability Method for Graph Neural Network with Generative Adversarial Nets 基于生成对抗网络的图神经网络的可解释性方法

Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition Pub Date : 2022-11-17 DOI: 10.1145/3581807.3581850

Xinrui Kang, Dong Liang, Qinfeng Li

{"title":"GANExplainer: Explainability Method for Graph Neural Network with Generative Adversarial Nets","authors":"Xinrui Kang, Dong Liang, Qinfeng Li","doi":"10.1145/3581807.3581850","DOIUrl":"https://doi.org/10.1145/3581807.3581850","url":null,"abstract":"In recent years, graph neural networks (GNNs) have achieved encouraging performance in the processing of graph data generated in non-Euclidean space. GNNs learn node features by aggregating and combining neighbor information, which is applied to many graphics tasks. However, the complex deep learning structure is still regarded as a black box, which is difficult to obtain the full trust of human beings. Due to the lack of interpretability, the application of graph neural network is greatly limited. Therefore, we propose an interpretable method, called GANExplainer, to explain GNNs at the model level. Our method can implicitly generate the characteristic subgraph of the graph without relying on specific input examples as the interpretation of the model to the data. GANExplainer relies on the framework of generative-adversarial method to train the generator and discriminator at the same time. More importantly, when constructing the discriminator, the corresponding graph rules are added to ensure the effectiveness of the generated characteristic subgraph. We carried out experiments on synthetic dataset and chemical molecules dataset and verified the effect of our method on model level interpreter from three aspects: accuracy, fidelity and sparsity.","PeriodicalId":292813,"journal":{"name":"Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133542357","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

MMOT: Motion-Aware Multi-Object Tracking with Optical Flow MMOT:运动感知多目标跟踪与光流

Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition Pub Date : 2022-11-17 DOI: 10.1145/3581807.3581824

Haodong Liu, Tianyang Xu, Xiaojun Wu

引用次数: 0

Improved Fusion of Visual and Semantic Representations by Gated Co-Attention for Scene Text Recognition 基于门控共同注意的场景文本识别中视觉和语义表征的改进融合

Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition Pub Date : 2022-11-17 DOI: 10.1145/3581807.3581837

Junwei Zhou, Xi Wang, Jiao Dai, Jizhong Han

引用次数: 0

Semantic Maximum Relevance and Modal Alignment for Cross-Modal Retrieval 跨模态检索的语义最大关联和模态对齐

Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition Pub Date : 2022-11-17 DOI: 10.1145/3581807.3581857

Pingping Sun, Baohua Qiang, Zhiguang Liu, Xianyi Yang, Guangyong Xi, Weigang Liu, Ruidong Chen, S. Zhang

{"title":"Semantic Maximum Relevance and Modal Alignment for Cross-Modal Retrieval","authors":"Pingping Sun, Baohua Qiang, Zhiguang Liu, Xianyi Yang, Guangyong Xi, Weigang Liu, Ruidong Chen, S. Zhang","doi":"10.1145/3581807.3581857","DOIUrl":"https://doi.org/10.1145/3581807.3581857","url":null,"abstract":"With the increasing abundance of multimedia data resources, researches on mining the relationship between different modalities to achieve refined cross-modal retrieval are gradually emerging. In this paper, we propose a novel Semantic Maximum Relevance and Modal Alignment (SMR-MA) for Cross-Modal Retrieval, which utilizes the pre-trained model with abundant image text information to extract the features of each image text, and further promotes the modal information interaction between the same semantic categories through the modal alignment module and the multi-layer perceptron with shared weights. In addition, multi-modal embedding is distributed to the normalized hypersphere, and angular edge penalty is applied between feature embedding and weight in angular space to maximize the classification boundary, thus increasing both intra-class similarity and inter-class difference. Comprehensive analysis experiments on three benchmark datasets demonstrate that the proposed method has superior performance in cross-modal retrieval tasks and is significantly superior to the state-of-the-art cross-modal retrieval methods.","PeriodicalId":292813,"journal":{"name":"Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116152215","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 Phoneme Recognition using Attention-based Methods 基于注意的音素识别方法研究

Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition Pub Date : 2022-11-17 DOI: 10.1145/3581807.3581866

Yupei Zhang

引用次数: 0

Tongue Image Retrieval Based On Reinforcement Learning 基于强化学习的舌头图像检索

Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition Pub Date : 2022-11-17 DOI: 10.1145/3581807.3581848

A. Farooq, Xinfeng Zhang

{"title":"Tongue Image Retrieval Based On Reinforcement Learning","authors":"A. Farooq, Xinfeng Zhang","doi":"10.1145/3581807.3581848","DOIUrl":"https://doi.org/10.1145/3581807.3581848","url":null,"abstract":"In Chinese medicine, the patient's body constitution plays a crucial role in determining the course of treatment because it is so intrinsically linked to the patient's physiological and pathological processes. Traditional Chinese medicine practitioners use tongue diagnosis to determine a person's constitutional type during an examination. An effective solution is needed to overcome the complexity of this setting before the tongue image constitution recognition system can be deployed on a non-invasive mobile device for fast, efficient, and accurate constitution recognition. We will use deep deterministic policy gradients to implement tongue retrieval techniques. We suggested a new method for image retrieval systems based on Deep Deterministic Policy Gradients (DDPG) in an effort to boost the precision of database searches for query images. We present a strategy for enhancing image retrieval accuracy that uses the complexity of individual instances to split the dataset into two subsets for independent classification using Deep reinforcement learning. Experiments on tongue datasets are performed to gauge the efficacy of our suggested approach; in these experiments, deep reinforcement learning techniques are applied to develop a retrieval system for pictures of tongues affected by various disorders. Using our proposed strategy, it may be possible to enhance image retrieval accuracy through enhanced recognition of tongue diseases. Databases containing pictures of tongues affected by a wide range of disorders will be used as examples. The experimental results suggest that the new approach to computing the main colour histogram outperforms the prior one. Though the difference is tiny statistically, the enhanced retrieval impact is clear to the human eye. The tongue is similarly brought to the fore to emphasise the importance of the required verbal statement. Both investigations used tongue images classified into five distinct categories.","PeriodicalId":292813,"journal":{"name":"Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121905344","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

ResAsapp: An Effective Convolution to Distinguish Adjacent Pixels For Scene Text Detection ResAsapp:一种用于场景文本检测的有效卷积识别相邻像素

Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition Pub Date : 2022-11-17 DOI: 10.1145/3581807.3581854

Kangming Weng, X. Du, Kunze Chen, Dahan Wang, Shunzhi Zhu

引用次数: 0