with the development of 5G,the future wireless communication network tends to be more and more intelligent.In the face of new service de-mands of communication in the future such as super-heterogeneous network,multipl...with the development of 5G,the future wireless communication network tends to be more and more intelligent.In the face of new service de-mands of communication in the future such as super-heterogeneous network,multiple communication sce-narios,large number of antenna elements and large bandwidth,new theories and technologies of intelli-gent communication have been widely studied,among which Deep Learning(DL)is a powerful technology in artificial intelligence(AI).It can be trained to con-tinuously learn to update the optimal parameters.This paper reviews the latest research progress of DL in in-telligent communication,and emphatically introduces five scenarios including Cognitive Radio(CR),Edge Computing(EC),Channel Measurement(CM),End to end Encoder/Decoder(EED)and Visible Light Com-munication(VLC).The prospect and challenges of further research and development in the future are also discussed.展开更多
Artificial intelligence has significantly altered many job workflows, hence expanding earlier notions of limitations, outcomes, size, and prices. GeoAI is a multidisciplinary field that encompasses computer science, e...Artificial intelligence has significantly altered many job workflows, hence expanding earlier notions of limitations, outcomes, size, and prices. GeoAI is a multidisciplinary field that encompasses computer science, engineering, statistics, and spatial science. Because this subject focuses on real-world issues, it has a significant impact on society and the economy. A broad context incorporating fundamental questions of theory, epistemology, and the scientific method is used to bring artificial intelligence (Al) and geography together. This connection has the potential to have far-reaching implications for the geographic study. GeoAI, or the combination of geography with artificial intelligence, offers unique solutions to a variety of smart city issues. This paper provides an overview of GeoAI technology, including the definition of GeoAI and the differences between GeoAI and traditional AI. Key steps to successful geographic data analysis include integrating AI with GIS and using GeoAI tools and technologies. Also shown are key areas of applications and models in GeoAI, likewise challenges to adopt GeoAI methods and technology as well as benefits. This article also included a case study on the use of GeoAI in Kuwait, as well as a number of recommendations.展开更多
The topographic information of a closed world is expressed as a graph. The plural mov- ingobjects which go and back in it according to a single moving strategy are supposed.The moving strategy is expressed by numerica...The topographic information of a closed world is expressed as a graph. The plural mov- ingobjects which go and back in it according to a single moving strategy are supposed.The moving strategy is expressed by numerical values as a decision table. Coding is performed with this table as chromosomes, and this is optimized by using genetic algorithm. These environments were realized on a computer, and the simulation was carried out. As the result, the learning of the method to act so that moving objects do not obstruct mutually was recognized, and it was confirmed that these methods are effective for optimizing moving strategy.展开更多
Traffic identification becomes more important,yet more challenging as related encryption techniques are rapidly developing nowadays.Unlike recent deep learning methods that apply image processing to solve such encrypt...Traffic identification becomes more important,yet more challenging as related encryption techniques are rapidly developing nowadays.Unlike recent deep learning methods that apply image processing to solve such encrypted traffic problems,in this pa⁃per,we propose a method named Payload Encoding Representation from Transformer(PERT)to perform automatic traffic feature extraction using a state-of-the-art dynamic word embedding technique.By implementing traffic classification experiments on a pub⁃lic encrypted traffic data set and our captured Android HTTPS traffic,we prove the pro⁃posed method can achieve an obvious better effectiveness than other compared baselines.To the best of our knowledge,this is the first time the encrypted traffic classification with the dynamic word embedding has been addressed.展开更多
The Gleason grade group(GG)is an important basis for assessing the malignancy of prostate can-cer,but it requires invasive biopsy to obtain pathology.To noninvasively evaluate GG,an automatic prediction method is prop...The Gleason grade group(GG)is an important basis for assessing the malignancy of prostate can-cer,but it requires invasive biopsy to obtain pathology.To noninvasively evaluate GG,an automatic prediction method is proposed based on multi-scale convolutional neural network of the ensemble attention module trained with curriculum learning.First,a lesion-attention map based on the image of the region of interest is proposed in combination with the bottleneck attention module to make the network more focus on the lesion area.Second,the feature pyramid network is combined to make the network better learn the multi-scale information of the lesion area.Finally,in the network training,a curriculum based on the consistency gap between the visual evaluation and the pathological grade is proposed,which further improves the prediction performance of the network.Ex-perimental results show that the proposed method is better than the traditional network model in predicting GG performance.The quadratic weighted Kappa is 0.4711 and the positive predictive value for predicting clinically significant cancer is 0.9369.展开更多
基金the National Nat-ural Science Foundation of China under Grant No.62061039Postgraduate Innovation Project of Ningxia University No.JIP20210076Key project of Ningxia Natural Science Foundation No.2020AAC02006.
文摘with the development of 5G,the future wireless communication network tends to be more and more intelligent.In the face of new service de-mands of communication in the future such as super-heterogeneous network,multiple communication sce-narios,large number of antenna elements and large bandwidth,new theories and technologies of intelli-gent communication have been widely studied,among which Deep Learning(DL)is a powerful technology in artificial intelligence(AI).It can be trained to con-tinuously learn to update the optimal parameters.This paper reviews the latest research progress of DL in in-telligent communication,and emphatically introduces five scenarios including Cognitive Radio(CR),Edge Computing(EC),Channel Measurement(CM),End to end Encoder/Decoder(EED)and Visible Light Com-munication(VLC).The prospect and challenges of further research and development in the future are also discussed.
文摘Artificial intelligence has significantly altered many job workflows, hence expanding earlier notions of limitations, outcomes, size, and prices. GeoAI is a multidisciplinary field that encompasses computer science, engineering, statistics, and spatial science. Because this subject focuses on real-world issues, it has a significant impact on society and the economy. A broad context incorporating fundamental questions of theory, epistemology, and the scientific method is used to bring artificial intelligence (Al) and geography together. This connection has the potential to have far-reaching implications for the geographic study. GeoAI, or the combination of geography with artificial intelligence, offers unique solutions to a variety of smart city issues. This paper provides an overview of GeoAI technology, including the definition of GeoAI and the differences between GeoAI and traditional AI. Key steps to successful geographic data analysis include integrating AI with GIS and using GeoAI tools and technologies. Also shown are key areas of applications and models in GeoAI, likewise challenges to adopt GeoAI methods and technology as well as benefits. This article also included a case study on the use of GeoAI in Kuwait, as well as a number of recommendations.
文摘The topographic information of a closed world is expressed as a graph. The plural mov- ingobjects which go and back in it according to a single moving strategy are supposed.The moving strategy is expressed by numerical values as a decision table. Coding is performed with this table as chromosomes, and this is optimized by using genetic algorithm. These environments were realized on a computer, and the simulation was carried out. As the result, the learning of the method to act so that moving objects do not obstruct mutually was recognized, and it was confirmed that these methods are effective for optimizing moving strategy.
文摘Traffic identification becomes more important,yet more challenging as related encryption techniques are rapidly developing nowadays.Unlike recent deep learning methods that apply image processing to solve such encrypted traffic problems,in this pa⁃per,we propose a method named Payload Encoding Representation from Transformer(PERT)to perform automatic traffic feature extraction using a state-of-the-art dynamic word embedding technique.By implementing traffic classification experiments on a pub⁃lic encrypted traffic data set and our captured Android HTTPS traffic,we prove the pro⁃posed method can achieve an obvious better effectiveness than other compared baselines.To the best of our knowledge,this is the first time the encrypted traffic classification with the dynamic word embedding has been addressed.
基金Foundation item:the Suzhou Municipal Health and Family Planning Commission's Key Diseases Diagnosis and Treatment Program(No.LCzX202001)the Science and Technology Development Project ofSuzhou(Nos.SS2019012andSKY2021031)+1 种基金the Youth Innovation Promotion Association CAS(No.2021324)the Medical Research Project of Jiangsu Provincial Health and Family Planning Commission(No.M2020068)。
文摘The Gleason grade group(GG)is an important basis for assessing the malignancy of prostate can-cer,but it requires invasive biopsy to obtain pathology.To noninvasively evaluate GG,an automatic prediction method is proposed based on multi-scale convolutional neural network of the ensemble attention module trained with curriculum learning.First,a lesion-attention map based on the image of the region of interest is proposed in combination with the bottleneck attention module to make the network more focus on the lesion area.Second,the feature pyramid network is combined to make the network better learn the multi-scale information of the lesion area.Finally,in the network training,a curriculum based on the consistency gap between the visual evaluation and the pathological grade is proposed,which further improves the prediction performance of the network.Ex-perimental results show that the proposed method is better than the traditional network model in predicting GG performance.The quadratic weighted Kappa is 0.4711 and the positive predictive value for predicting clinically significant cancer is 0.9369.
