Pub Date : 2020-06-01DOI: 10.1109/iwecai50956.2020.00001
{"title":"[Title page i]","authors":"","doi":"10.1109/iwecai50956.2020.00001","DOIUrl":"https://doi.org/10.1109/iwecai50956.2020.00001","url":null,"abstract":"","PeriodicalId":364789,"journal":{"name":"2020 International Workshop on Electronic Communication and Artificial Intelligence (IWECAI)","volume":"385 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115911569","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 : 2020-06-01DOI: 10.1109/IWECAI50956.2020.00022
Hongying Bai, Xiaotong Zhang
Dynamic adaptability of resource scheduling in Wireless Sensor Networks (WSN) is required due to the limited energy of sensor nodes and impact of harsh environment. In this paper, considering the dynamic changes of network topology and energy saving, combined with the advantages of centralized and distributed resource scheduling, a new Dynamic Hybrid Resource Scheduling (DHRS) for WSN is proposed. In the early stage of network running, we apply centralized scheduling. In the later period of network operation, sensor nodes are failed frequently and the topology changes dynamically. When Dynamic Change Factor (DCF) exceeds threshold, we apply distributed resource scheduling. The simulation results show that the DHRS can dynamically adapt to topology changes, reduce energy consumption of nodes, and extend the life cycle of WSN.
{"title":"Dynamic Hybrid Resource Scheduling for WSN","authors":"Hongying Bai, Xiaotong Zhang","doi":"10.1109/IWECAI50956.2020.00022","DOIUrl":"https://doi.org/10.1109/IWECAI50956.2020.00022","url":null,"abstract":"Dynamic adaptability of resource scheduling in Wireless Sensor Networks (WSN) is required due to the limited energy of sensor nodes and impact of harsh environment. In this paper, considering the dynamic changes of network topology and energy saving, combined with the advantages of centralized and distributed resource scheduling, a new Dynamic Hybrid Resource Scheduling (DHRS) for WSN is proposed. In the early stage of network running, we apply centralized scheduling. In the later period of network operation, sensor nodes are failed frequently and the topology changes dynamically. When Dynamic Change Factor (DCF) exceeds threshold, we apply distributed resource scheduling. The simulation results show that the DHRS can dynamically adapt to topology changes, reduce energy consumption of nodes, and extend the life cycle of WSN.","PeriodicalId":364789,"journal":{"name":"2020 International Workshop on Electronic Communication and Artificial Intelligence (IWECAI)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126782834","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 : 2020-06-01DOI: 10.1109/IWECAI50956.2020.00034
Zhechuan Wang, Yuping Wang
Density Peaks Clustering (DPC) tries to use two objectives: density and peaks, to automatically determine the number of clusters. It is claimed to be applicable to data sets with non-spherical clusters. However, the cutoff distance dc in DPC should be determined based on the experience of decision maker and the cluster centers should be selected manually. But it is very difficult to do so and improper selection of these will result in incorrect results. In order to overcome these shortcomings, an adaptive cutoff distance computing method based on Gini index is proposed firstly, and then the possibility (i.e., multiplication of the local density and the relative distance y=ρiδi) of each point xi as a cluster center is calculated, moreover, the point with the maximal change of possibility is determined as the critical point. Each point whose possibility is larger than that of the critical point will be a cluster center. In this way, both the number of clusters and cluster centers can be automatically determined, and the manually selecting the cluster centers through the decision graph in DPC can be avoided. Based on these, a new density peak clustering algorithm by automatically determining both the number of clusters and cluster centers is proposed. Finally, experiments are conducted and the results show that the new algorithm can not only automatically determine the cluster center, but also has higher accuracy than DPC.
{"title":"A New Density Peak Clustering Algorithm for Automatically Determining Clustering Centers","authors":"Zhechuan Wang, Yuping Wang","doi":"10.1109/IWECAI50956.2020.00034","DOIUrl":"https://doi.org/10.1109/IWECAI50956.2020.00034","url":null,"abstract":"Density Peaks Clustering (DPC) tries to use two objectives: density and peaks, to automatically determine the number of clusters. It is claimed to be applicable to data sets with non-spherical clusters. However, the cutoff distance dc in DPC should be determined based on the experience of decision maker and the cluster centers should be selected manually. But it is very difficult to do so and improper selection of these will result in incorrect results. In order to overcome these shortcomings, an adaptive cutoff distance computing method based on Gini index is proposed firstly, and then the possibility (i.e., multiplication of the local density and the relative distance y=ρiδi) of each point xi as a cluster center is calculated, moreover, the point with the maximal change of possibility is determined as the critical point. Each point whose possibility is larger than that of the critical point will be a cluster center. In this way, both the number of clusters and cluster centers can be automatically determined, and the manually selecting the cluster centers through the decision graph in DPC can be avoided. Based on these, a new density peak clustering algorithm by automatically determining both the number of clusters and cluster centers is proposed. Finally, experiments are conducted and the results show that the new algorithm can not only automatically determine the cluster center, but also has higher accuracy than DPC.","PeriodicalId":364789,"journal":{"name":"2020 International Workshop on Electronic Communication and Artificial Intelligence (IWECAI)","volume":"158 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122027759","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 : 2020-06-01DOI: 10.1109/IWECAI50956.2020.00020
Su ZhaoPeng, Zhou Kuanjiu, Cui Kai, Hu Shaoqi
Heterogeneous computing, as a kind of special parallel computing method, can exert the ability of different computing resources based on the characteristics of computing tasks and is much advantageous in improving server computing performance, energy efficiency ratio (EER) and real time performance. FPGA-GPU-CPU heterogeneous computing was born for the real-time processing of massive of data. However, the communication bottlenecks between different computing units have set restrictions on the computing capabilities of heterogeneous platform. In view of the above issues, this article connects GPU and FPGA devices through the PCI Express bus, so that data can be transmitted between these heterogeneous computing units without the assistance of the system CPU memory. And, we have realized that the PCIe communication by taking FPGA as the main controller through GPUDirect RDMA, which improves the weakness of slow reading in PCle communication where the GPU as the main controller. Experiments show that we have improved the efficiency by 1.4 times compared to the memory sharing-based communication and the data rate has been made closest to the maximum theoretical bandwidth.
{"title":"PCIE-Based High-Performance FPGA-GPU-CPU Heterogeneous Communication Method","authors":"Su ZhaoPeng, Zhou Kuanjiu, Cui Kai, Hu Shaoqi","doi":"10.1109/IWECAI50956.2020.00020","DOIUrl":"https://doi.org/10.1109/IWECAI50956.2020.00020","url":null,"abstract":"Heterogeneous computing, as a kind of special parallel computing method, can exert the ability of different computing resources based on the characteristics of computing tasks and is much advantageous in improving server computing performance, energy efficiency ratio (EER) and real time performance. FPGA-GPU-CPU heterogeneous computing was born for the real-time processing of massive of data. However, the communication bottlenecks between different computing units have set restrictions on the computing capabilities of heterogeneous platform. In view of the above issues, this article connects GPU and FPGA devices through the PCI Express bus, so that data can be transmitted between these heterogeneous computing units without the assistance of the system CPU memory. And, we have realized that the PCIe communication by taking FPGA as the main controller through GPUDirect RDMA, which improves the weakness of slow reading in PCle communication where the GPU as the main controller. Experiments show that we have improved the efficiency by 1.4 times compared to the memory sharing-based communication and the data rate has been made closest to the maximum theoretical bandwidth.","PeriodicalId":364789,"journal":{"name":"2020 International Workshop on Electronic Communication and Artificial Intelligence (IWECAI)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121444085","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 : 2020-06-01DOI: 10.1109/IWECAI50956.2020.00032
Y. Wang, Yanmei Yang
With the continuous progress of the times, various sectors of society are developing rapidly. Agriculture, as a pillar industry of the national economy, has an important impact on society. As an important economic crop, cotton has been planted in the traditional way, which is no longer suitable for the future development direction. In the future, smart agriculture will dominate. This paper makes a brief introduction to smart agriculture, summarizes and summarizes the main application technologies of smart agriculture in cotton production management, enumerates and analyses the actual application of smart agriculture in cotton production management. On this basis, the advantages and disadvantages of smart agriculture in cotton production management are analyzed, and the summary and prospects are made.
{"title":"Research on Application of Smart Agriculture in Cotton Production Management","authors":"Y. Wang, Yanmei Yang","doi":"10.1109/IWECAI50956.2020.00032","DOIUrl":"https://doi.org/10.1109/IWECAI50956.2020.00032","url":null,"abstract":"With the continuous progress of the times, various sectors of society are developing rapidly. Agriculture, as a pillar industry of the national economy, has an important impact on society. As an important economic crop, cotton has been planted in the traditional way, which is no longer suitable for the future development direction. In the future, smart agriculture will dominate. This paper makes a brief introduction to smart agriculture, summarizes and summarizes the main application technologies of smart agriculture in cotton production management, enumerates and analyses the actual application of smart agriculture in cotton production management. On this basis, the advantages and disadvantages of smart agriculture in cotton production management are analyzed, and the summary and prospects are made.","PeriodicalId":364789,"journal":{"name":"2020 International Workshop on Electronic Communication and Artificial Intelligence (IWECAI)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114268551","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 : 2020-06-01DOI: 10.1109/IWECAI50956.2020.00009
Fan HanYang, Fan Hongming, Gao Ruiyuan
Aiming at the current fact of low recognition rate and poor anti-noise performance of the existing air target maneuver recognition algorithms, a method of target maneuver recognition based on LSTM network was studied. Input of the LSTM network is getting by a series of preprocessing on the original track, including eliminating outliers and interpolation, and reconstructing the track. After training and recognition, the maneuver type recognition result of the target to be measured is obtained. By comparing with HMM model algorithm, the algorithm designed in this paper turns out to be of higher recognition rate and better anti-noise performance under the same training sample and test set.
{"title":"Research on Air Target Maneuver Recognition Based on LSTM Network","authors":"Fan HanYang, Fan Hongming, Gao Ruiyuan","doi":"10.1109/IWECAI50956.2020.00009","DOIUrl":"https://doi.org/10.1109/IWECAI50956.2020.00009","url":null,"abstract":"Aiming at the current fact of low recognition rate and poor anti-noise performance of the existing air target maneuver recognition algorithms, a method of target maneuver recognition based on LSTM network was studied. Input of the LSTM network is getting by a series of preprocessing on the original track, including eliminating outliers and interpolation, and reconstructing the track. After training and recognition, the maneuver type recognition result of the target to be measured is obtained. By comparing with HMM model algorithm, the algorithm designed in this paper turns out to be of higher recognition rate and better anti-noise performance under the same training sample and test set.","PeriodicalId":364789,"journal":{"name":"2020 International Workshop on Electronic Communication and Artificial Intelligence (IWECAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130874538","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 : 2020-06-01DOI: 10.1109/IWECAI50956.2020.00039
Ke Zhao, Yongan Lu, Zhizheng Zhang, Wei Wang
When the appearance of a target changes dramatically, the traditional tracking methods can no longer be used for target tracking task. This paper proposes an adaptive visual tracking algorithm based on key frame selection and reinforcement learning (RL) to solve this problem. First of all, the probability value of the RL network output is analyzed, and the predicted value of output is normalized. At the model update stage of each frame, the probability value corresponding to the optimal behavior is judged. If it conforms to the preset rules, the last frame of the current frame is set as the key frame, and the network model is fine-tuned by using the key frame. The proposed algorithm is only fine-tuned in key frames to obtain multiple fixed prediction models. The experiment is conducted on 100 video sequences of the Object Tracking Benchmark to verify the effectiveness of key frame selection strategy. Compared with the original reinforcement learning based tracking algorithm, the tracking accuracy and the success rate of the proposed algorithm are improved respectively.
{"title":"Adaptive Visual Tracking Based on Key Frame Selection and Reinforcement Learning","authors":"Ke Zhao, Yongan Lu, Zhizheng Zhang, Wei Wang","doi":"10.1109/IWECAI50956.2020.00039","DOIUrl":"https://doi.org/10.1109/IWECAI50956.2020.00039","url":null,"abstract":"When the appearance of a target changes dramatically, the traditional tracking methods can no longer be used for target tracking task. This paper proposes an adaptive visual tracking algorithm based on key frame selection and reinforcement learning (RL) to solve this problem. First of all, the probability value of the RL network output is analyzed, and the predicted value of output is normalized. At the model update stage of each frame, the probability value corresponding to the optimal behavior is judged. If it conforms to the preset rules, the last frame of the current frame is set as the key frame, and the network model is fine-tuned by using the key frame. The proposed algorithm is only fine-tuned in key frames to obtain multiple fixed prediction models. The experiment is conducted on 100 video sequences of the Object Tracking Benchmark to verify the effectiveness of key frame selection strategy. Compared with the original reinforcement learning based tracking algorithm, the tracking accuracy and the success rate of the proposed algorithm are improved respectively.","PeriodicalId":364789,"journal":{"name":"2020 International Workshop on Electronic Communication and Artificial Intelligence (IWECAI)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127088751","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}
With the outbreak of COVID-19 at the end of 2019, under the requirement of in-depth study and implementation of the overall national security concept, people's health level has become the focus of people's attention, and it is also the most basic and fundamental important indicator to reflect people's livelihood. Taking Shenzhen, a city with strong comprehensive economic level, as an example, this paper uses data processing to select six major influencing factors, such as medical treatment and environment, and uses the method of regression and fitting crossover analysis to establish the fitting curve between factors and people's health level for prediction, and obtains the regression equation. On this basis, T-S Fuzzy Neural Network (T-S FNN) is used to divide the evaluation grade of regression model, make an effective evaluation of multiple factors of people's physical health level, establish a comprehensive prediction evaluation model, and obtain the gradient grade of factors affecting people's physical health correlation and their own direct factors.
{"title":"A Comprehensive Predictive Evaluation Model Based on T-S Fuzzy Neural Network and Regression Fitting Cross Analysis","authors":"Yunai Wu, Jia Zhang, Jiankai Zuo, Yuqi Tan, Zhixuan Han, Zhen Zhao","doi":"10.1109/IWECAI50956.2020.00045","DOIUrl":"https://doi.org/10.1109/IWECAI50956.2020.00045","url":null,"abstract":"With the outbreak of COVID-19 at the end of 2019, under the requirement of in-depth study and implementation of the overall national security concept, people's health level has become the focus of people's attention, and it is also the most basic and fundamental important indicator to reflect people's livelihood. Taking Shenzhen, a city with strong comprehensive economic level, as an example, this paper uses data processing to select six major influencing factors, such as medical treatment and environment, and uses the method of regression and fitting crossover analysis to establish the fitting curve between factors and people's health level for prediction, and obtains the regression equation. On this basis, T-S Fuzzy Neural Network (T-S FNN) is used to divide the evaluation grade of regression model, make an effective evaluation of multiple factors of people's physical health level, establish a comprehensive prediction evaluation model, and obtain the gradient grade of factors affecting people's physical health correlation and their own direct factors.","PeriodicalId":364789,"journal":{"name":"2020 International Workshop on Electronic Communication and Artificial Intelligence (IWECAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130893187","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 : 2020-06-01DOI: 10.1109/IWECAI50956.2020.00038
Yanrui Du, Weixiang Zhao
Named entity recognition (also known as entity recognition, entity segmentation and entity extraction) is a sub task of information extraction. It aims to locate and classify named entities in text into predefined categories, such as people, organization, location, time expression, etc. Compared with English, there are more unsolved problems in Chinese named entity recognition. Named entities in English have obvious formal signs, that is, the first letter of every word in entities should be capitalized, and entity boundary recognition is relatively easy. Compared with English, the task of Chinese named entity recognition is more complex, and the recognition of entity boundary is more difficult. In this paper, we propose a named entity method by adding the word position, which embeds the word position of each word into the word vector, in order to better recognize the boundary of Chinese named entity. The experimental results show that the F1 value of the named entity recognition method proposed in this paper increases by about 1%.
{"title":"Named Entity Recognition Method with Word Position","authors":"Yanrui Du, Weixiang Zhao","doi":"10.1109/IWECAI50956.2020.00038","DOIUrl":"https://doi.org/10.1109/IWECAI50956.2020.00038","url":null,"abstract":"Named entity recognition (also known as entity recognition, entity segmentation and entity extraction) is a sub task of information extraction. It aims to locate and classify named entities in text into predefined categories, such as people, organization, location, time expression, etc. Compared with English, there are more unsolved problems in Chinese named entity recognition. Named entities in English have obvious formal signs, that is, the first letter of every word in entities should be capitalized, and entity boundary recognition is relatively easy. Compared with English, the task of Chinese named entity recognition is more complex, and the recognition of entity boundary is more difficult. In this paper, we propose a named entity method by adding the word position, which embeds the word position of each word into the word vector, in order to better recognize the boundary of Chinese named entity. The experimental results show that the F1 value of the named entity recognition method proposed in this paper increases by about 1%.","PeriodicalId":364789,"journal":{"name":"2020 International Workshop on Electronic Communication and Artificial Intelligence (IWECAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114163983","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}
This paper introduces a design method of 1PPS signal generation and synchronization module which can be realized on FPGA, and uses the high stability 10MHz of cesium atomic clock as the global clock to generate 1PPS signal. When the external reference 1PPS signal is input, the internal 1PPS phase can be synchronized with the reference signal to realize the phase adjustment. The method is verified on spartan6 xcslx9 FPGA, which can generate 1PPS signal meeting the accuracy requirements and synchronize with external reference. This module was integrated into LIP Cs-3000 cesium atomic clock and verified.
{"title":"Design of a Cesium Atomic Clock 1PPS Signal Generation and Synchronization Module Based on FPGA","authors":"Jianxiang Wang, Jingzhong Cui, Shiwei Wang, Pei Ma, Yonggang Guo, Zhidong Liu, Liang Chang","doi":"10.1109/IWECAI50956.2020.00016","DOIUrl":"https://doi.org/10.1109/IWECAI50956.2020.00016","url":null,"abstract":"This paper introduces a design method of 1PPS signal generation and synchronization module which can be realized on FPGA, and uses the high stability 10MHz of cesium atomic clock as the global clock to generate 1PPS signal. When the external reference 1PPS signal is input, the internal 1PPS phase can be synchronized with the reference signal to realize the phase adjustment. The method is verified on spartan6 xcslx9 FPGA, which can generate 1PPS signal meeting the accuracy requirements and synchronize with external reference. This module was integrated into LIP Cs-3000 cesium atomic clock and verified.","PeriodicalId":364789,"journal":{"name":"2020 International Workshop on Electronic Communication and Artificial Intelligence (IWECAI)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129797685","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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