Pub Date : 2019-04-01DOI: 10.1109/ICIASE45644.2019.9074050
Zongchao Yi, Qilin Cai, Tianfan Chen, Yun Zhang
Mold design is one of the core skills for students major in mold design and manufacturing. In this paper, augmented reality (AR) system is introduced to teach students on the acquisition of mold inner structure design. The AR teaching system can be deployed in mobile terminals such as intelligent mobile phone or pad, at which the students can rotate and scale the three dimensional (3D) models, read explanatory notes and play the voice illustrations.
{"title":"AR System for Mold Design Teaching","authors":"Zongchao Yi, Qilin Cai, Tianfan Chen, Yun Zhang","doi":"10.1109/ICIASE45644.2019.9074050","DOIUrl":"https://doi.org/10.1109/ICIASE45644.2019.9074050","url":null,"abstract":"Mold design is one of the core skills for students major in mold design and manufacturing. In this paper, augmented reality (AR) system is introduced to teach students on the acquisition of mold inner structure design. The AR teaching system can be deployed in mobile terminals such as intelligent mobile phone or pad, at which the students can rotate and scale the three dimensional (3D) models, read explanatory notes and play the voice illustrations.","PeriodicalId":206741,"journal":{"name":"2019 IEEE International Conference of Intelligent Applied Systems on Engineering (ICIASE)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127091263","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}
Pub Date : 2019-04-01DOI: 10.1109/ICIASE45644.2019.9074094
Huanzhao Li, Aimei Zhang, Xue-Yang Pei
To reduce the influence of thermal error on the accuracy of CNC machine tool this paper proposed a temperature sensor measuring point optimization method based on DBSCAN clustering algorithm and a BP neural network modeling method for CNC machine tool. DBSCAN algorithm and Pearson correlation coefficient method reduced the temperature measurement point from 16 to 5. Established BP neural network for temperature and spindle displacement, and the score of the model up to 0.99, which provided an important theoretical basis for the machine tool thermal error compensation.
{"title":"Research on Thermal Error of CNC Machine Tool Based on DBSCAN Clustering and BP Neural Network Algorithm","authors":"Huanzhao Li, Aimei Zhang, Xue-Yang Pei","doi":"10.1109/ICIASE45644.2019.9074094","DOIUrl":"https://doi.org/10.1109/ICIASE45644.2019.9074094","url":null,"abstract":"To reduce the influence of thermal error on the accuracy of CNC machine tool this paper proposed a temperature sensor measuring point optimization method based on DBSCAN clustering algorithm and a BP neural network modeling method for CNC machine tool. DBSCAN algorithm and Pearson correlation coefficient method reduced the temperature measurement point from 16 to 5. Established BP neural network for temperature and spindle displacement, and the score of the model up to 0.99, which provided an important theoretical basis for the machine tool thermal error compensation.","PeriodicalId":206741,"journal":{"name":"2019 IEEE International Conference of Intelligent Applied Systems on Engineering (ICIASE)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114447525","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}
Pub Date : 2019-04-01DOI: 10.1109/ICIASE45644.2019.9074070
Wei-Chiang Wu
Conventional downlink non-orthogonal multiple access (NOMA) exploits the power domain for multiple access and applies successive interference cancellation (SIC) to mitigate intra-cluster interference. It requires sophisticated user terminals (UTs) clustering, moreover, imperfect SIC results in error propagation that severely degrades system performance. In this paper, a novel code-aided multiuser multiple-input multiple-output NOMA (MIMO-NOMA) framework for downlink transmission is developed. We propose a simple grouping algorithm that separate all UTs in a cell into several clusters, with cluster number less than or equal to the BS antenna array size. ZF-based multiuser beamforming is then employed to remove the inter-cluster interference. Computer simulation results verify that the proposed method outperforms the conventional schemes.
{"title":"A Novel Code-Aided Non-Orthogonal Multiple Access Technique in Downlink MIMO System","authors":"Wei-Chiang Wu","doi":"10.1109/ICIASE45644.2019.9074070","DOIUrl":"https://doi.org/10.1109/ICIASE45644.2019.9074070","url":null,"abstract":"Conventional downlink non-orthogonal multiple access (NOMA) exploits the power domain for multiple access and applies successive interference cancellation (SIC) to mitigate intra-cluster interference. It requires sophisticated user terminals (UTs) clustering, moreover, imperfect SIC results in error propagation that severely degrades system performance. In this paper, a novel code-aided multiuser multiple-input multiple-output NOMA (MIMO-NOMA) framework for downlink transmission is developed. We propose a simple grouping algorithm that separate all UTs in a cell into several clusters, with cluster number less than or equal to the BS antenna array size. ZF-based multiuser beamforming is then employed to remove the inter-cluster interference. Computer simulation results verify that the proposed method outperforms the conventional schemes.","PeriodicalId":206741,"journal":{"name":"2019 IEEE International Conference of Intelligent Applied Systems on Engineering (ICIASE)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126926653","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}
Pub Date : 2019-04-01DOI: 10.1109/ICIASE45644.2019.9074081
Bing Liu, Jiancheng Zou
By analyzing the Linux kernel, this paper designs a network packet processing system based on the Linux Ethernet Bridge. The system builds a platform with functions of high-performance data acquisition, analysis, control and forwarding. The Ipv4/Ipv6 authentication and accounting system has the characteristics of high speed, safety and reliability.
{"title":"The Network Accounting System with Security Certificate Based on the Eth ernet Bridge Ipv4/Ipv6","authors":"Bing Liu, Jiancheng Zou","doi":"10.1109/ICIASE45644.2019.9074081","DOIUrl":"https://doi.org/10.1109/ICIASE45644.2019.9074081","url":null,"abstract":"By analyzing the Linux kernel, this paper designs a network packet processing system based on the Linux Ethernet Bridge. The system builds a platform with functions of high-performance data acquisition, analysis, control and forwarding. The Ipv4/Ipv6 authentication and accounting system has the characteristics of high speed, safety and reliability.","PeriodicalId":206741,"journal":{"name":"2019 IEEE International Conference of Intelligent Applied Systems on Engineering (ICIASE)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121996985","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}
Pub Date : 2019-04-01DOI: 10.1109/ICIASE45644.2019.9074049
Hui Liang, Jianxing Wu, Ran Wang, F. Liang, Li Sun, Guohe Zhang
Spiking neural networks (SNNs) are artificial neural network models that are closely mimic natural neural networks. LIF (Leaky Integrate-and-fire) neuron model, population coding and Tempotron supervised learning rules are used to construct a spiking neural network for visual color feature classification based on RGB-HSV (Red, Green, Blue -Hue, Saturation, Value) model. The product of a momentum learning rate and the last weight change is proposed to speed up the training of the SNN. Test results based on a common data set show that the accuracy of the SNN can be up to 90%.
脉冲神经网络是一种近似于自然神经网络的人工神经网络模型。在RGB-HSV (Red, Green, Blue -Hue, Saturation, Value)模型的基础上，利用LIF (Leaky Integrate-and-fire)神经元模型、种群编码和Tempotron监督学习规则构建了用于视觉颜色特征分类的峰值神经网络。为了加快SNN的训练速度，提出了动量学习率与最后一次权值变化的乘积。基于通用数据集的测试结果表明，该SNN的准确率可达90%。
{"title":"A Spiking Neural Network for Visual Color Feature Classification for Pictures with RGB-HSV Model","authors":"Hui Liang, Jianxing Wu, Ran Wang, F. Liang, Li Sun, Guohe Zhang","doi":"10.1109/ICIASE45644.2019.9074049","DOIUrl":"https://doi.org/10.1109/ICIASE45644.2019.9074049","url":null,"abstract":"Spiking neural networks (SNNs) are artificial neural network models that are closely mimic natural neural networks. LIF (Leaky Integrate-and-fire) neuron model, population coding and Tempotron supervised learning rules are used to construct a spiking neural network for visual color feature classification based on RGB-HSV (Red, Green, Blue -Hue, Saturation, Value) model. The product of a momentum learning rate and the last weight change is proposed to speed up the training of the SNN. Test results based on a common data set show that the accuracy of the SNN can be up to 90%.","PeriodicalId":206741,"journal":{"name":"2019 IEEE International Conference of Intelligent Applied Systems on Engineering (ICIASE)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116814725","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}
Pub Date : 2019-04-01DOI: 10.1109/ICIASE45644.2019.9074015
Yanjuan Wang, H. Niu, Xiao Wang, Liang Chen
In order to improve the accurate recognition rate and localization rate of multi-class object detection, a new network structure, Res-YOLO-R., based on the combination of Residual Network (ResNet) and You Only Look Once (YOLO) detection network, is proposed. To improve the location ability and speed up the convergence of the network, the number and size of prediction boxes for YOLO network are redesigned by clustering analysis algorithm. Removing part of the pool layer and using convolution layer to raise or reduce the dimension of the feature to improve the ability of feature extraction and computing of the network. ResNet is designed as the feature extraction part, and the final average pool layer and the full connection layer are removed, and combines with the improved YOLO detection network to improve the degradation problem caused by the increasement of the network depth. In order to make the network learn object context information better, the ROUTE and REORG layers are used to fuse feature from different layers, and the feature map is reorganized. Through the comparison of experiments on commodity data sets, the network structure can effectively reduce the false detection rate and miss detection rate, improve the detection accuracy, positioning ability and recall rate of commodities, and have good real-time and generalization ability and strong practicability.
为了提高多类目标检测的准确识别率和定位率，提出了一种新的网络结构Res-YOLO-R。提出了一种基于残余网络(ResNet)和You Only Look Once (YOLO)检测网络相结合的检测网络。为了提高网络的定位能力，加快网络的收敛速度，利用聚类分析算法重新设计了YOLO网络预测盒的数量和大小。去除部分池层，利用卷积层提高或降低特征维数，提高网络的特征提取和计算能力。设计了ResNet作为特征提取部分，去除最终的平均池层和全连接层，结合改进的YOLO检测网络，改善了网络深度增加带来的退化问题。为了使网络更好地学习对象上下文信息，利用ROUTE层和REORG层融合不同层的特征，对特征映射进行重组。通过对商品数据集的实验对比，该网络结构能够有效降低误检率和漏检率，提高商品的检测精度、定位能力和召回率，具有良好的实时性和泛化能力，实用性强。
{"title":"Multi-class Object Detection Algorithm Based on Convolutional Neural Network","authors":"Yanjuan Wang, H. Niu, Xiao Wang, Liang Chen","doi":"10.1109/ICIASE45644.2019.9074015","DOIUrl":"https://doi.org/10.1109/ICIASE45644.2019.9074015","url":null,"abstract":"In order to improve the accurate recognition rate and localization rate of multi-class object detection, a new network structure, Res-YOLO-R., based on the combination of Residual Network (ResNet) and You Only Look Once (YOLO) detection network, is proposed. To improve the location ability and speed up the convergence of the network, the number and size of prediction boxes for YOLO network are redesigned by clustering analysis algorithm. Removing part of the pool layer and using convolution layer to raise or reduce the dimension of the feature to improve the ability of feature extraction and computing of the network. ResNet is designed as the feature extraction part, and the final average pool layer and the full connection layer are removed, and combines with the improved YOLO detection network to improve the degradation problem caused by the increasement of the network depth. In order to make the network learn object context information better, the ROUTE and REORG layers are used to fuse feature from different layers, and the feature map is reorganized. Through the comparison of experiments on commodity data sets, the network structure can effectively reduce the false detection rate and miss detection rate, improve the detection accuracy, positioning ability and recall rate of commodities, and have good real-time and generalization ability and strong practicability.","PeriodicalId":206741,"journal":{"name":"2019 IEEE International Conference of Intelligent Applied Systems on Engineering (ICIASE)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134088767","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}
Pub Date : 2019-04-01DOI: 10.1109/ICIASE45644.2019.9074082
W. Myung, Zhao Qi, Ma Cheng
Network on Chip (NoC) that integrates a large number of nodes in a chip is a competitive candidate to solve the problems of multi-core chip scalability and clock synchronization. Among the many factors that determine the NoC’s performance, the routing algorithms that determine a path from a source node to destination node have a tremendous impact on it. Suitable routing method can greatly improve the performance of NoC. In this paper, we analyze the effect of routing algorithms commonly used in a mesh structure. We compared the data transmission in terms of latency and throughput. Furthermore, we compare which method yielded relatively good results in case of existing a failed node in the network. All results and analysis in the text are derived by using the Booksim simulator. This research proposes the direction of NoC routing algorithms.
片上网络(Network on Chip, NoC)将大量节点集成在一个芯片上，是解决多核芯片可扩展性和时钟同步问题的有力选择。在决定NoC性能的众多因素中，确定源节点到目标节点路径的路由算法对其影响巨大。合适的路由方法可以大大提高NoC的性能。本文分析了网格结构中常用的路由算法的效果。我们从延迟和吞吐量方面比较了数据传输。此外，我们比较了在网络中存在故障节点的情况下，哪种方法产生相对较好的结果。本文中的所有结果和分析都是使用Booksim模拟器得出的。本研究提出了NoC路由算法的发展方向。
{"title":"Performance Analysis of Routing Algorithms in Mesh Based Network on Chip using Booksim Simulator","authors":"W. Myung, Zhao Qi, Ma Cheng","doi":"10.1109/ICIASE45644.2019.9074082","DOIUrl":"https://doi.org/10.1109/ICIASE45644.2019.9074082","url":null,"abstract":"Network on Chip (NoC) that integrates a large number of nodes in a chip is a competitive candidate to solve the problems of multi-core chip scalability and clock synchronization. Among the many factors that determine the NoC’s performance, the routing algorithms that determine a path from a source node to destination node have a tremendous impact on it. Suitable routing method can greatly improve the performance of NoC. In this paper, we analyze the effect of routing algorithms commonly used in a mesh structure. We compared the data transmission in terms of latency and throughput. Furthermore, we compare which method yielded relatively good results in case of existing a failed node in the network. All results and analysis in the text are derived by using the Booksim simulator. This research proposes the direction of NoC routing algorithms.","PeriodicalId":206741,"journal":{"name":"2019 IEEE International Conference of Intelligent Applied Systems on Engineering (ICIASE)","volume":"219 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121294717","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}
Pub Date : 2019-04-01DOI: 10.1109/ICIASE45644.2019.9074104
Mengyue Zhang, Weihan Liao, Jianlian Zhang, Huisheng Gao, Fanyi Wang, Bin Lin
Based on neural network and local binary pattern algorithm, this paper builds a lightweight artificial face recognition system on chip Firefly-RK3399, with high speed, strong robustness and high recognition accuracy. Our embedded artificial intelligent face recognition system mainly consists of face detection, feature extraction and recognition. Multi-task convolutional neural network (MTCNN) under the CaffeOnACL framework is utilized for face detection, and the local binary pattern (LBP) is applied as face recognition algorithm. Experiments illustrate that our artificial intelligent embedded face recognition system has high speed and accuracy, which is easy-carrying and of high commercial value as well.
{"title":"Embedded Face Recognition System Based on Multi-task Convolutional Neural Network and LBP Features","authors":"Mengyue Zhang, Weihan Liao, Jianlian Zhang, Huisheng Gao, Fanyi Wang, Bin Lin","doi":"10.1109/ICIASE45644.2019.9074104","DOIUrl":"https://doi.org/10.1109/ICIASE45644.2019.9074104","url":null,"abstract":"Based on neural network and local binary pattern algorithm, this paper builds a lightweight artificial face recognition system on chip Firefly-RK3399, with high speed, strong robustness and high recognition accuracy. Our embedded artificial intelligent face recognition system mainly consists of face detection, feature extraction and recognition. Multi-task convolutional neural network (MTCNN) under the CaffeOnACL framework is utilized for face detection, and the local binary pattern (LBP) is applied as face recognition algorithm. Experiments illustrate that our artificial intelligent embedded face recognition system has high speed and accuracy, which is easy-carrying and of high commercial value as well.","PeriodicalId":206741,"journal":{"name":"2019 IEEE International Conference of Intelligent Applied Systems on Engineering (ICIASE)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122381652","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}
Pub Date : 2019-04-01DOI: 10.1109/ICIASE45644.2019.9074055
Ying-Yi Lai, Yun-Sheng Ho, T. Liang
In this paper, we proposed a wind speed and direction sensor which based on fiber Bragg grating (FBG). A broadband light source (BBS) was used as the light source. The anemometer consists of two sizes of stainless steel pipe and the coil spring designed to connect a cross-steel frame and the wind-pressed plate. When the wind speed drives the wind-pressed plate, the FBG will change the grating pitch through a special internal structure design to achieve the wind speed and wind direction sensing.
{"title":"Design a Wind Speed and Direction Sensor Based on Fiber Bragg Grating","authors":"Ying-Yi Lai, Yun-Sheng Ho, T. Liang","doi":"10.1109/ICIASE45644.2019.9074055","DOIUrl":"https://doi.org/10.1109/ICIASE45644.2019.9074055","url":null,"abstract":"In this paper, we proposed a wind speed and direction sensor which based on fiber Bragg grating (FBG). A broadband light source (BBS) was used as the light source. The anemometer consists of two sizes of stainless steel pipe and the coil spring designed to connect a cross-steel frame and the wind-pressed plate. When the wind speed drives the wind-pressed plate, the FBG will change the grating pitch through a special internal structure design to achieve the wind speed and wind direction sensing.","PeriodicalId":206741,"journal":{"name":"2019 IEEE International Conference of Intelligent Applied Systems on Engineering (ICIASE)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126010105","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}
Pub Date : 2019-04-01DOI: 10.1109/ICIASE45644.2019.9074009
Z. Lee, Chou-Yuan Lee, So-Tsung Chou, Wei-Ping Ma, Fulan Ye, Zhen Chen
Data mining means to find valuable information in database or data sets. For imbalanced data, there are extremely low number of samples in database or data sets and it is not easy to solve these problems by traditional methods of data mining. In this paper, a distributed intelligent algorithm is proposed to imbalanced data. Apache Spark is implemented as the distributed framework in the proposed distributed intelligent algorithm, and its cluster computing framework with in-memory data processing engine can do analytic on large volumes of data. In the distributed framework, Apache Spark with synthetic minority oversampling technique (SMOTE) is proposed to process imbalanced data first. Thereafter, the support vector machine (SVM) is used to classify imbalanced data. The zoo data set from UCI repository is used to verify the correctness of the proposed algorithm. The results of the proposed distributed intelligent algorithm can get better performance than these compared traditional classifiers.
{"title":"A Distributed Intelligent Algorithm Applied to Imbalanced Data","authors":"Z. Lee, Chou-Yuan Lee, So-Tsung Chou, Wei-Ping Ma, Fulan Ye, Zhen Chen","doi":"10.1109/ICIASE45644.2019.9074009","DOIUrl":"https://doi.org/10.1109/ICIASE45644.2019.9074009","url":null,"abstract":"Data mining means to find valuable information in database or data sets. For imbalanced data, there are extremely low number of samples in database or data sets and it is not easy to solve these problems by traditional methods of data mining. In this paper, a distributed intelligent algorithm is proposed to imbalanced data. Apache Spark is implemented as the distributed framework in the proposed distributed intelligent algorithm, and its cluster computing framework with in-memory data processing engine can do analytic on large volumes of data. In the distributed framework, Apache Spark with synthetic minority oversampling technique (SMOTE) is proposed to process imbalanced data first. Thereafter, the support vector machine (SVM) is used to classify imbalanced data. The zoo data set from UCI repository is used to verify the correctness of the proposed algorithm. The results of the proposed distributed intelligent algorithm can get better performance than these compared traditional classifiers.","PeriodicalId":206741,"journal":{"name":"2019 IEEE International Conference of Intelligent Applied Systems on Engineering (ICIASE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131046790","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}
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