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2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)最新文献

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Structural Multi-solution Optimization and Dynamics Analysis of The Main Roller Shaft of Bolt 螺栓主滚子轴结构多解优化及动力学分析
2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2021-07-01 DOI: 10.1109/MLISE54096.2021.00109
Jian Xu, Zhen-Feng Yang
{"title":"Structural Multi-solution Optimization and Dynamics Analysis of The Main Roller Shaft of Bolt","authors":"Jian Xu, Zhen-Feng Yang","doi":"10.1109/MLISE54096.2021.00109","DOIUrl":"https://doi.org/10.1109/MLISE54096.2021.00109","url":null,"abstract":"In order to Gatling gun's main roller shaft, it shows by using the force analysis. It shows the rotation speed of the main roller shaft is non-constant, it can be determined by the cam curve and the clearance fit between the main roller and the cam curve. The speed of the revolution and position of the main roller shaft was raised and dropped in the core hole. The main roller shaft is sometimes integrated with the bolt movement, but basically, it makes that the main roller only deforms itself, so the main roller shaft is a method for the Gatling cannon. The Gatling cannon needs the main roller shaft more than the Gatling gun. In Gatling cannon, the main roller shaft must be sure to exist. The main roller shaft and bolt body should be two components, not one component. This is the caliber greater than 20mm scheme, and not for the caliber less than 20mm design.","PeriodicalId":430495,"journal":{"name":"2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129710237","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 Method to Cloud Computing Resources Requirement Prediction on SaaS Application 基于SaaS应用的云计算资源需求预测方法
2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2021-07-01 DOI: 10.1109/MLISE54096.2021.00027
Jun He, Sunyan Hong, Caiqing Zhang, Yan Liu, Fei Deng, Jing Yu
{"title":"A Method to Cloud Computing Resources Requirement Prediction on SaaS Application","authors":"Jun He, Sunyan Hong, Caiqing Zhang, Yan Liu, Fei Deng, Jing Yu","doi":"10.1109/MLISE54096.2021.00027","DOIUrl":"https://doi.org/10.1109/MLISE54096.2021.00027","url":null,"abstract":"Nowadays, it is more important than ever before to accurately predict the cloud computing resource requirement on Software as Service (SaaS) applications. However, the extant methods for requirement prediction are largely designed from the perspective of cloud resource providers, which just results in the insufficient scalability of the models. To solve the insufficiency, a novel method described as Extended Coupled Hidden Markov Model (ECHMM) is proposed in this paper. Based on the historical monitoring data, this paper pioneers to take into account two factors of software evolution and resources correlations in the prediction. Combined with the characteristics of SaaS, it is thus possible for the construction of the relationship between multiple observation states and hidden states. Baum-Welch algorithm is used for the training of the model parameters and the obtaining of the optimal solution. And on a typical SaaS application platform, a cloud computing resource prediction experiment is carried out. The experiment shows that a fuller range of cloud computing resources: the prediction result of the model is closer to the real value. The prediction errors of CPU., memory and network resources are reduced by 2%, 1.7% and 4.5% respectively. And the model can catch an “abnormal” state and get it corrected in time for the increase of scalability.","PeriodicalId":430495,"journal":{"name":"2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116279548","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
An Improved Adaptive Sliding Mode Control of a Robotic Manipulator with Hysteresis Nonlinearity Based on Adaptive Speed Factor 基于自适应速度因子的迟滞非线性机器人自适应滑模控制
2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2021-07-01 DOI: 10.1109/MLISE54096.2021.00107
L. Tan, GuoDong Li, Jian Li, Kexin Hu, R. Dong, Minshan Feng, Li-guo Zhu
{"title":"An Improved Adaptive Sliding Mode Control of a Robotic Manipulator with Hysteresis Nonlinearity Based on Adaptive Speed Factor","authors":"L. Tan, GuoDong Li, Jian Li, Kexin Hu, R. Dong, Minshan Feng, Li-guo Zhu","doi":"10.1109/MLISE54096.2021.00107","DOIUrl":"https://doi.org/10.1109/MLISE54096.2021.00107","url":null,"abstract":"This paper presents a novel adaptive sliding mode control design for a robotic manipulator with backlash hysteresis nonlinearity and uncertainties in actuator dynamics. A radial basis function (RBF) neural network (NN) is used to approximate the dynamic robotic terms and the robust term in the proposed adaptive sliding mode controller can compensate for the hysteresis nonlinearity. Aiming at the online-tuning problem for proportional-differential (PD) controller gains, the self-coupling PD control with adaptive speed factor (ASF) is proposed, which helps the system to achieve better performance on the tracking and smoother torque outputs. And the comparison with three different controllers is presented in the paper. Theoretical analysis and simulation results have proved the effectiveness of the control scheme.","PeriodicalId":430495,"journal":{"name":"2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128675970","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: 1
Cob Angle Recognition Algorithm Based On Machine Vision 基于机器视觉的Cob角识别算法
2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2021-07-01 DOI: 10.1109/MLISE54096.2021.00074
Lu Kai, Ma Minghan, Liu Yuehui, Hu Weiqiang
{"title":"Cob Angle Recognition Algorithm Based On Machine Vision","authors":"Lu Kai, Ma Minghan, Liu Yuehui, Hu Weiqiang","doi":"10.1109/MLISE54096.2021.00074","DOIUrl":"https://doi.org/10.1109/MLISE54096.2021.00074","url":null,"abstract":"In order to solve the problem of lens skew when some products of single channel cob coupling machine are coupled in actual production, the recognition algorithm of lens and PCB in cob optical module based on machine vision is studied. Industrial camera is used to collect lens and PCB image. Halcon software is used to preprocess lens and PCB image and detect edge to identify the target. Finally this papaer calculates the angle difference between lens and PCB. Then, through the platform verification, the results show that: the recognition of lens and PCB and the calculation of angle difference can be realized in 0.5 seconds, and the recognition effect can still be guaranteed when there are miscellaneous points in the background, and the repetition accuracy can reach 0.05 ° and the error between the measured value and the actual value is within 0.2 ° which meets the requirements of industrial production. This research has a certain reference significance for visual recognition in the actual production of cob optical module.","PeriodicalId":430495,"journal":{"name":"2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123975055","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: 2
An Adaptive Network-Based Fuzzy Inference System to Intellectual Property Risk Assessment in Crowdsourcing Design 基于自适应网络的众包设计中知识产权风险评估模糊推理系统
2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2021-07-01 DOI: 10.1109/MLISE54096.2021.00048
Chao Yin, Ligao Pan, Xiaobin Li
{"title":"An Adaptive Network-Based Fuzzy Inference System to Intellectual Property Risk Assessment in Crowdsourcing Design","authors":"Chao Yin, Ligao Pan, Xiaobin Li","doi":"10.1109/MLISE54096.2021.00048","DOIUrl":"https://doi.org/10.1109/MLISE54096.2021.00048","url":null,"abstract":"The crowdsourcing design service model provides an effective way for the R&D and innovation of the manufacturing industry and the personalized and service-oriented development of products. Intellectual property is the core resource in the crowdsourcing design service model, which is faced with various risks due to the lack of effective control. In this context, this paper proposes an intellectual property risk assessment method based on an adaptive network-based fuzzy inference system (ANFIS) in crowdsourcing design. First of all, the influencing factors of intellectual property risk in crowdsourcing design are analyzed, and a set of intellectual property risk assessment index system is constructed. Secondly, a risk assessment model based on ANFIS is established to quantitatively assess intellectual property risks in crowdsourcing design. Finally, the proposed ANFIS model has been trained and evaluated, and the experimental results show that the method can achieve accurate evaluation of intellectual property risks.","PeriodicalId":430495,"journal":{"name":"2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)","volume":"9 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113934815","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
Structure design of ankle knee rehabilitation training robot 踝关节康复训练机器人结构设计
2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2021-07-01 DOI: 10.1109/MLISE54096.2021.00079
H. Jie, Ying Chang, Shuo Wang, Wenxuan Liu, Quanchao Wei, Xilong Wang, Nan Lin
{"title":"Structure design of ankle knee rehabilitation training robot","authors":"H. Jie, Ying Chang, Shuo Wang, Wenxuan Liu, Quanchao Wei, Xilong Wang, Nan Lin","doi":"10.1109/MLISE54096.2021.00079","DOIUrl":"https://doi.org/10.1109/MLISE54096.2021.00079","url":null,"abstract":"Rehabilitation training robot has been widely used in the market, especially the lower limb rehabilitation training robot, which plays a very important role in human activities. At present, many rehabilitation institutions and researchers use the lower limb rehabilitation training robot for lower limb rehabilitation training. In this paper, by combining the characteristics of the current lower limb rehabilitation training robot, we propose an ankle and knee rehabilitation training robot independently developed by our team. The robot can carry out knee rehabilitation training alone or ankle rehabilitation training to form an ankle and knee rehabilitation training robot. This paper will briefly introduce the structure design of ankle and knee rehabilitation robot, explain some of the mechanisms and performance, and briefly introduce the connection mode of the two robots. It provides a reference for the future research of ankle and knee rehabilitation training robot.","PeriodicalId":430495,"journal":{"name":"2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134591218","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 Digital Design and Manufacturing Technology of Mould Based on CAD Software 基于CAD软件的模具数字化设计与制造技术研究
2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2021-07-01 DOI: 10.1109/MLISE54096.2021.00055
Guangjun Gong
{"title":"Research on Digital Design and Manufacturing Technology of Mould Based on CAD Software","authors":"Guangjun Gong","doi":"10.1109/MLISE54096.2021.00055","DOIUrl":"https://doi.org/10.1109/MLISE54096.2021.00055","url":null,"abstract":"The traditional mode relies too much on the fitter centered operation mode and production management mode, and this mode has long construction period, low efficiency and high labor cost, which is difficult to match and meet the needs of modern industrial production. Based on this, this paper first analyses the mold digital design and manufacturing technology based on computer software, then studies the mold design and manufacturing technology development trend of computer CAD software, and finally gives the specific application of mold digital design and manufacturing based on computer CAD software.","PeriodicalId":430495,"journal":{"name":"2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)","volume":"185 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134037634","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 SVM gait recognition based on single acceleration sensor 基于单加速度传感器的SVM步态识别研究
2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2021-07-01 DOI: 10.1109/MLISE54096.2021.00033
Ying Chang, Lan Wang, Lingjie Lin, Ming Liu
{"title":"Research on SVM gait recognition based on single acceleration sensor","authors":"Ying Chang, Lan Wang, Lingjie Lin, Ming Liu","doi":"10.1109/MLISE54096.2021.00033","DOIUrl":"https://doi.org/10.1109/MLISE54096.2021.00033","url":null,"abstract":"In this paper, a method of human motion recognition based on single position sensor with multiple eigenvalues is proposed. The data are analyzed in time domain and frequency domain respectively, and then nine related features are extracted by feature engineering. The experiment compares the pattern recognition methods of belt, pants pocket and coat pocket based on SVM, and classifies five kinds of motion patterns: walking, running, climbing stairs, descending stairs and relaxing. The results show that the comprehensive application of the six features has the best recognition effect, which can reach more than 98%.","PeriodicalId":430495,"journal":{"name":"2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123986558","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
Modular Attention Network based on Language Model for Referring Expression 基于指称表达语言模型的模块化注意网络
2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2021-07-01 DOI: 10.1109/MLISE54096.2021.00023
Xutao Deng, Liang Xie, Yibo Cui, Meishan Zhang, Huijiong Yan, Ye Yan, Erwei Yin
{"title":"Modular Attention Network based on Language Model for Referring Expression","authors":"Xutao Deng, Liang Xie, Yibo Cui, Meishan Zhang, Huijiong Yan, Ye Yan, Erwei Yin","doi":"10.1109/MLISE54096.2021.00023","DOIUrl":"https://doi.org/10.1109/MLISE54096.2021.00023","url":null,"abstract":"Referring expression comprehension is to point out the corresponding object in the image by giving the natural language expression. This requires not only a full understanding of the image content, but also a certain understanding of the relationship between the objects in the text information, such as the understanding of the object appearance, location attributes and the relationship between the objects. In this paper, we propose a module network guided by scene graph and based on pre-training model (pre-training SGAN). The image is modeled as a semantic graph of graph structure representation, and the natural language expression is also analyzed as the representation of language scene diagram. Further, we use attention to extract the feature from different modules. Finally, the image and text are matched by similarity. The experiment shows that our method is better than other algorithms.","PeriodicalId":430495,"journal":{"name":"2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114065531","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
SRM: A Sequential Recommendation Model with Convolutional Neural Network and Multiple Features 基于卷积神经网络和多特征的顺序推荐模型
2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2021-07-01 DOI: 10.1109/MLISE54096.2021.00017
Haixin Huang, Yue Wang
{"title":"SRM: A Sequential Recommendation Model with Convolutional Neural Network and Multiple Features","authors":"Haixin Huang, Yue Wang","doi":"10.1109/MLISE54096.2021.00017","DOIUrl":"https://doi.org/10.1109/MLISE54096.2021.00017","url":null,"abstract":"The sequential recommendation predicts the items that a user may interact in the near future via modeling the user behavior sequences. However, the existing methods adopt shallow networks which cannot extract deep features, and can't fully integrate the user general preferences. We proposed a model named a sequential recommendation model with convolutional neural network and multiple features (SRM), which uses multi-layer convolutional neural network to capture both the point-to-point and union-type sequential patterns. In addition, the model adds the user general preferences and item attributes to improve feature diversity. The experiments on public dataset proved that the SRM model can effectively predict the user's future actions, and the performance of the model is better than the existing sequential recommendation models.","PeriodicalId":430495,"journal":{"name":"2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132127878","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: 1
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