Proceedings of the 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence最新文献

Deep joint convolutional neural network with double-level attention mechanism for multi-sensor bearing performance degradation assessment 基于双层次注意机制的深度联合卷积神经网络多传感器轴承性能退化评估

Proceedings of the 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2021-12-22 DOI: 10.1145/3508546.3508648

Jiachen Kuang, Guanghua Xu, T. Tao, Chongyue Yang, Fan Wei

{"title":"Deep joint convolutional neural network with double-level attention mechanism for multi-sensor bearing performance degradation assessment","authors":"Jiachen Kuang, Guanghua Xu, T. Tao, Chongyue Yang, Fan Wei","doi":"10.1145/3508546.3508648","DOIUrl":"https://doi.org/10.1145/3508546.3508648","url":null,"abstract":"The deep learning methods with data fusion are promising to deal with the performance degradation assessment (PDA) of rotating machinery with multi-sensor data reliably. However, there are still two challenges: (1) each sensor that is mounted at a different position makes a different contribution to the task, (2) there is much conflicting information between the signature owing to strong background noise. To address these two challenges, a deep joint convolutional neural network (DJ-CNN) including the feature extractor and the predictor is proposed for intelligent PDA tasks. Within this framework, multi-sensor data are input to the feature extractor network in parallel. Then, the predictor, whose attention module refines and recalibrates the feature maps in sensor-wise attention and signal-wise attention, is trained with input being multi-sensor data again. Finally, the trained DJ-CNN, which not only could naturally extract deep features from raw multi-sensor but also enhances the more important parts of feature maps in a double-level attention structure, is constructed for performance degradation assessment. The effectiveness and superiority of the proposed DJ-CNN are demonstrated on a run-to-failure bearing experiment.","PeriodicalId":300032,"journal":{"name":"Proceedings of the 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115012125","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: 4

An Overview of Behavioral Cooperative Control of Multiple Unmanned Surface Vehicles 多水面无人驾驶车辆行为协同控制研究综述

Proceedings of the 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2021-12-22 DOI: 10.1145/3508546.3508560

Yang Liu, Zheng Wang, Yang Yin, Quanshun Yang

引用次数: 2

A reinforcement learning algorithm for two-dimensional irregular packing problems 二维不规则布局问题的强化学习算法

Proceedings of the 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2021-12-22 DOI: 10.1145/3508546.3508614

Jie Fang, Yunqing Rao, Xiaoqiang Guo, Xusheng Zhao

引用次数: 5

An Interactive Elements Layout Design Method Based on Fusion of Dynamic Fuzzy Genetic Mechanism and Ergonomics Principles for Complex System 基于动态模糊遗传机制与人机工程学原理融合的复杂系统交互元素布局设计方法

Proceedings of the 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2021-12-22 DOI: 10.1145/3508546.3508646

Kun Yu, Jie Huang, Jinfeng Tao

{"title":"An Interactive Elements Layout Design Method Based on Fusion of Dynamic Fuzzy Genetic Mechanism and Ergonomics Principles for Complex System","authors":"Kun Yu, Jie Huang, Jinfeng Tao","doi":"10.1145/3508546.3508646","DOIUrl":"https://doi.org/10.1145/3508546.3508646","url":null,"abstract":"For the direct influence caused by human errors in the process of human-machine interaction of complex system, a interactive elements layout design method for complex system is proposed. Firstly, the ergonomic layout principles are introduced, which are respectively used in the interaction elements layout design phase of complex system. Secondly, the sorting coefficient of interaction elements from three aspects: operational sort, operational frequency and importance degree are quantitative calculation. Thirdly, an improved dynamic fuzzy genetic mechanism is constructed to optimize the sorting solutions of interactive elements. And fuzzy theory is integrated in genetic mechanism, to improved the layout solutions searching efficiency and enlarge the searching area and depth. The improved dynamic fuzzy genetic operations are used to produce new offspring individuals on the selection, crossover and mutation operations in populations. Finally, the objective evaluation method and subjective evaluation method are proposed to evaluate the original solution and the optimized solution of interactive elements layout design. This design method can quantify the analysis of interactive elements, and has more scientific and intuitive guidance for data analysis and optimization of interactive elements. It provides a new design idea for the interaction elements layout design for complex systems.","PeriodicalId":300032,"journal":{"name":"Proceedings of the 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125188066","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

A Fast Optimization Algorithm Based on Multi-RBF For High-Dimensional and Computationally Expensive Problems 一种基于多rbf的高维计算量大问题快速优化算法

Proceedings of the 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2021-12-22 DOI: 10.1145/3508546.3508551

Meng-Ting Wu, Lingling Wang, Qianyi Lu, Pengjie Hu, Jin Xu

{"title":"A Fast Optimization Algorithm Based on Multi-RBF For High-Dimensional and Computationally Expensive Problems","authors":"Meng-Ting Wu, Lingling Wang, Qianyi Lu, Pengjie Hu, Jin Xu","doi":"10.1145/3508546.3508551","DOIUrl":"https://doi.org/10.1145/3508546.3508551","url":null,"abstract":"Optimization problems of the numerical model in the engineering field usually involve many undetermined parameters and computationally expensive simulations. Using evolutionary algorithms alone is inefficient because it takes up thousands of engineering simulations to obtain a good solution. Recently, surrogate-assisted evolutionary algorithms have been widely researched, which train a surrogate model (GP, RBF, SVM) to replace mostly computation of origin engineering model. But most surrogate model-based algorithms still require more than 1000 times evaluations to get reasonable solutions by origin model. To further improve optimization efficiency, a new ensemble surrogate modeling-based and some novel in-fill strategies-assisted fast optimization algorithm (ESMO) is proposed for solving high-dimensional and computationally expensive problems in this work. ESMO employs Radial Basis Function Neural Network (RBF) as a surrogate model. ESMO prepares three different methods to build multi-RBF and corresponding in-fill strategies to trade-off exploration and exploitation. To guarantee fast optimization, a constraint-region method is also applied. Empirical studies demonstrate that ESMO shows much better performance than other state-of-the-art algorithms within 500 function evaluations.","PeriodicalId":300032,"journal":{"name":"Proceedings of the 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125267656","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

Dual Partial Recurrent Networks for Hyperspectral Image Change Detection 高光谱图像变化检测的双部分递归网络

Proceedings of the 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2021-12-22 DOI: 10.1145/3508546.3508616

Xiaochen Yuan, Jinlong Li

引用次数: 1

MFDNN: Mixed Features Deep Neural Network Model for Prompt-independent Automated Essay Scoring 基于混合特征的深度神经网络自动作文评分模型

Proceedings of the 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2021-12-22 DOI: 10.1145/3508546.3508629

Chang Liu, Gejian Ding

{"title":"MFDNN: Mixed Features Deep Neural Network Model for Prompt-independent Automated Essay Scoring","authors":"Chang Liu, Gejian Ding","doi":"10.1145/3508546.3508629","DOIUrl":"https://doi.org/10.1145/3508546.3508629","url":null,"abstract":"Most of the existing Automatic Essay Scoring (AES) models are prompt-dependent models that need the rated essays of specific prompt for training. However, there are few studies on prompt-independent AES. This paper studies how to fully use the effective prompt-dependent features to solve the prompt-independent AES problem. Different from the common method of only extracts multiple features, we consider reducing the interference between different features. We propose a new feature, called the deep dependent feature, which is extracted from the essay by a deep neural network. It is the representative feature that can distinguish the prompt label of the essay and has less overlap and conflict with other features. Firstly, we pre-scored the unrated target prompt data to generate pseudo data based on the manually extracted features. Then we build a new model, which is training on pseudo data to learn prompt-dependent information. Our model considers relevance feature, syntactic feature, and deep dependent feature. The performance of our model is evaluated on ASAP datasets, and the results show that our model outperforms the existing methods for prompt-independent AES.","PeriodicalId":300032,"journal":{"name":"Proceedings of the 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128497360","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

RWRel: A fast training framework for random walk-based knowledge graph embedding RWRel:基于随机行走的知识图嵌入快速训练框架

Proceedings of the 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2021-12-22 DOI: 10.1145/3508546.3508613

Hui Zhang, Da. Duan, Qiushi. Zhang

引用次数: 0

Research and application of recommendation algorithm based on bidirectional attention model 基于双向注意模型的推荐算法研究与应用

Proceedings of the 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2021-12-22 DOI: 10.1145/3508546.3508553

Jiahua Wan, Cheng-rui Ji, Yiwen Zhang

{"title":"Research and application of recommendation algorithm based on bidirectional attention model","authors":"Jiahua Wan, Cheng-rui Ji, Yiwen Zhang","doi":"10.1145/3508546.3508553","DOIUrl":"https://doi.org/10.1145/3508546.3508553","url":null,"abstract":"Since the beginning of the 20th century, with the continuous development of computer technology, more and more people began to use the convenience of computer to improve efficiency, but just because a large number of Internet users continue to increase, there is also a situation of information overload, which will lead to some problems, such as the huge amount of data, the extraction and utilization of effective information will increase Difficulties. The second is the previous recommendation algorithm, most of which predict through the score, but if only through the score, it will not make full use of other data information in the data set.In order to make the experimental results more convincing, this experiment uses a recommendation algorithm based on two-way attention model. First, the movie attributes and user attributes are processed by Convolutional Neural Network (CNN), and then through the full connection layer and the built attention model, effective information is obtained. Finally, the predicted score is generated and compared with the real score, and the final result is obtained Fruit.This experiment uses the movielens data set, by changing the parameters to affect the experimental results, so as to determine the final value of each parameter. This experiment is a comparative experiment, the recommendation algorithm with two-way attention model is compared with many previous algorithms, and the final conclusion is drawn. The experimental results are expressed by RMSE and MAE. According to the index results, the recommendation algorithm proposed in this experiment has better performance.","PeriodicalId":300032,"journal":{"name":"Proceedings of the 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130292615","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

The m6A RNA methylation regulators related transcriptome for identification of pancreatic cancer subtypes and prognostic markers m6A RNA甲基化调控因子与胰腺癌亚型和预后标志物鉴定相关的转录组

Proceedings of the 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2021-12-22 DOI: 10.1145/3508546.3508584

Zhengshu Lu, Yanrui Ding

{"title":"The m6A RNA methylation regulators related transcriptome for identification of pancreatic cancer subtypes and prognostic markers","authors":"Zhengshu Lu, Yanrui Ding","doi":"10.1145/3508546.3508584","DOIUrl":"https://doi.org/10.1145/3508546.3508584","url":null,"abstract":"N6-methyladenosine (m6A) methylation is a major epigenetic modification of RNA that affects processes such as translation of related mRNAs and non-coding RNAs. A large number of recent studies have shown that m6A modifications play a crucial role in cancer development, however, the prognostic value of the m6A associated transcriptome in pancreatic cancer has rarely been investigated. The purpose of this study is to investigate the prognostic markers and prognostic subtypes of m6A RNA methylation regulators related transcriptome in pancreatic ductal adenocarcinoma (PDAC). First, we identified the m6A RNA methylation regulators related prognostic transcriptome by Pearson correlation analysis and univariate cox regression. Subsequently, to explore key prognostic markers from the prognostic transcriptome, we proposed a G-P model based on greedy algorithms and pruning algorithms to obtain a set of key genes CASC11, KRT14, PDZD4, and identified two high/low-risk subtypes of PDAC with significant prognostic differences based on key genes. The clustering Silhouette coefficients was 0.99 for the key genes. In addition, CASC11 and KRT14 were strongly upregulated in the high-risk subtype and PDZD4 was upregulated in the low-risk subtype, and their differential expression was significantly associated with survival. In conclusion, we revealed the typing role and prognostic value of the m6A RNA methylation regulators-associated transcriptome in PDAC and provided new insights for identifying predictive biomarkers and therapeutic targets for PDAC.","PeriodicalId":300032,"journal":{"name":"Proceedings of the 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134390916","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