With the increasing amount of information on the internet,recommendation system(RS)has been utilized in a variety of fields as an efficient tool to overcome information overload.In recent years,the application of RS f...With the increasing amount of information on the internet,recommendation system(RS)has been utilized in a variety of fields as an efficient tool to overcome information overload.In recent years,the application of RS for health has become a growing research topic due to its tremendous advantages in providing appropriate recommendations and helping people make the right decisions relating to their health.This paper aims at presenting a comprehensive review of typical recommendation techniques and their applications in the field of healthcare.More concretely,an overview is provided on three famous recommendation techniques,namely,content-based,collaborative filtering(CF)-based,and hybrid methods.Next,we provide a snapshot of five application scenarios about health RS,which are dietary recommendation,lifestyle recommendation,training recommendation,decision-making for patients and physicians,and disease-related prediction.Finally,some key challenges are given with clear justifications to this new and booming field.展开更多
过高的峰均比(PAPR,peak-to-average power ratio)是正交频分复用技术(OFDM,Orthogonal Frequency-Di-vision Multiplexing)的一个主要缺陷。PAPR抑制技术的应用可以最大限度地减小的非线性失真,提高功率放大器(HPA,High Power Amplifi...过高的峰均比(PAPR,peak-to-average power ratio)是正交频分复用技术(OFDM,Orthogonal Frequency-Di-vision Multiplexing)的一个主要缺陷。PAPR抑制技术的应用可以最大限度地减小的非线性失真,提高功率放大器(HPA,High Power Amplifier)的效率。在OFDM系统中,随着载波数的增加,PAPR性能会变差。本文使用了一种削波滤波(CF,Clipping&Filtering)与星座图扩展(ACE,Active Constellation Extension)相结合的方法,可以在无数据率损失的情况下减小PAPR。在本方案中,计算出一个标准OFDM信号的PAPR数据作为参考。先对信号进行CF操作,把信号的PAPR值限制到很低的幅度,再用星座图扩展的方法恢复MER指标,就可以让信号进入非线性功放之前降低PAPR,并保证带外噪声符合发射机的要求。最后,把经过处理的OFDM信号和原始OFDM信号同时送入不同功率回退(IBO)参数的HPA进行了对比。实验结果表明,把信号送入功率回退为7dB的HPA,经过PAPR抑制算法处理的信号带肩比可以改善2dB以上,MER改善1dB以上。展开更多
In the seismic profile interpretation process,as the seismic data are big and the small geological features are difficult to identify,improvement of the efficiency is needed. In this study,structure tensor method in c...In the seismic profile interpretation process,as the seismic data are big and the small geological features are difficult to identify,improvement of the efficiency is needed. In this study,structure tensor method in computer image edge detection processing is applied into the 2D seismic profile. Coherent attribute is used to extract formation edge. At the same time,extracting the eigenvalues and eigenvectors to calculate the seismic geometric properties which include dip and apparent dip,automatic identification is achieved. Testing the Gaussian kernel function with synthetic models and comparing the coherent attribute and dip attribute extraction results before and after,the conclusion that Gaussian filter can remove the random noise is obtained.展开更多
Matrix factorization （MF） has been proved to be a very effective technique for collaborative filtering （ CF）, and hence has been widely adopted in today＇s recommender systems, Yet due to its lack of consideration...Matrix factorization （MF） has been proved to be a very effective technique for collaborative filtering （ CF）, and hence has been widely adopted in today＇s recommender systems, Yet due to its lack of consideration of the users＇ and items＇ local structures, the recommendation accuracy is not fully satisfied. By taking the trusts among users＇ and between items＇ effect on rating information into consideration, trust-aware recommendation systems （TARS） made a relatively good performance. In this paper, a method of incorporating trust into MF was proposed by building user-based and item-based implicit trust network under different contexts and implementing two implicit trust-based context-aware MF （]TMF） models. Experimental results proved the effectiveness of the methods.展开更多
协同过滤(CF)算法基于物品之间或用户之间的相似度能实现个性化推荐,然而CF算法普遍存在数据稀疏性的问题。针对用户‒物品评分稀疏问题,为使预测更加准确,提出一种基于协同训练与Boosting的协同过滤算法(CFCTB)。首先,利用协同训练将两...协同过滤(CF)算法基于物品之间或用户之间的相似度能实现个性化推荐,然而CF算法普遍存在数据稀疏性的问题。针对用户‒物品评分稀疏问题,为使预测更加准确,提出一种基于协同训练与Boosting的协同过滤算法(CFCTB)。首先,利用协同训练将两种CF集成于一个框架,两种CF互相添加置信度高的伪标记样本到对方的训练集中,并利用Boosting加权训练数据辅助协同训练;其次,采用加权集成预测最终的用户评分,有效避免伪标记样本所产生的噪声累加,进一步提高推荐性能。实验结果表明,在4个公开数据集上,所提算法的准确率优于单模型;在稀疏度最高的CiaoDVD数据集上,与面向推荐系统的全局和局部核(GLocal-K)相比,所提算法的平均绝对误差(MAE)降低了4.737%;与ECoRec(Ensemble of Co-trained Recommenders)算法相比,所提算法的均方根误差(RMSE)降低了7.421%。以上结果验证了所提算法的有效性。展开更多
Aiming at the problem of the peak to average power ratio(PAPR)in coherent optical orthogonal frequency division multiplexing(CO-OFDM),a hybrid PAPR reduction technique of the CO-OFDM system by combining iterative part...Aiming at the problem of the peak to average power ratio(PAPR)in coherent optical orthogonal frequency division multiplexing(CO-OFDM),a hybrid PAPR reduction technique of the CO-OFDM system by combining iterative partial transmit sequence(IPTS)scheme with modified clipping and filtering(MCF)is proposed.The simulation results show that at the complementary cumulative distribution function(CCDF)of 10^(-4),the PAPR of proposed scheme is optimized by 1.86 d B and 2.13 d B compared with those of IPTS and CF schemes,respectively.Meanwhile,when the bit error rate(BER)is 10^(-3),the optical signal to noise ratio(OSNR)are optimized by 1.57 dB and 0.66 d B compared with those of CF and IPTS-CF schemes,respectively.展开更多
基金National Natural Science Foundation of China(No.62003237)Tianjin Enterprise Science and Technology Commissioner Project(No.20YDTPJC01700)Tianjing Municipal Education Commission Scientific Research Project(No.2017ZD15)。
基金supported in part by the National Natural Science Foundation of China(61873148,61933007)the Royal Society of the UKthe Alexander von Humboldt Foundation of Germany。
文摘With the increasing amount of information on the internet,recommendation system(RS)has been utilized in a variety of fields as an efficient tool to overcome information overload.In recent years,the application of RS for health has become a growing research topic due to its tremendous advantages in providing appropriate recommendations and helping people make the right decisions relating to their health.This paper aims at presenting a comprehensive review of typical recommendation techniques and their applications in the field of healthcare.More concretely,an overview is provided on three famous recommendation techniques,namely,content-based,collaborative filtering(CF)-based,and hybrid methods.Next,we provide a snapshot of five application scenarios about health RS,which are dietary recommendation,lifestyle recommendation,training recommendation,decision-making for patients and physicians,and disease-related prediction.Finally,some key challenges are given with clear justifications to this new and booming field.
文摘过高的峰均比(PAPR,peak-to-average power ratio)是正交频分复用技术(OFDM,Orthogonal Frequency-Di-vision Multiplexing)的一个主要缺陷。PAPR抑制技术的应用可以最大限度地减小的非线性失真,提高功率放大器(HPA,High Power Amplifier)的效率。在OFDM系统中,随着载波数的增加,PAPR性能会变差。本文使用了一种削波滤波(CF,Clipping&Filtering)与星座图扩展(ACE,Active Constellation Extension)相结合的方法,可以在无数据率损失的情况下减小PAPR。在本方案中,计算出一个标准OFDM信号的PAPR数据作为参考。先对信号进行CF操作,把信号的PAPR值限制到很低的幅度,再用星座图扩展的方法恢复MER指标,就可以让信号进入非线性功放之前降低PAPR,并保证带外噪声符合发射机的要求。最后,把经过处理的OFDM信号和原始OFDM信号同时送入不同功率回退(IBO)参数的HPA进行了对比。实验结果表明,把信号送入功率回退为7dB的HPA,经过PAPR抑制算法处理的信号带肩比可以改善2dB以上,MER改善1dB以上。
基金Support by National Natural Science Foundation of China(No.41274120)
文摘In the seismic profile interpretation process,as the seismic data are big and the small geological features are difficult to identify,improvement of the efficiency is needed. In this study,structure tensor method in computer image edge detection processing is applied into the 2D seismic profile. Coherent attribute is used to extract formation edge. At the same time,extracting the eigenvalues and eigenvectors to calculate the seismic geometric properties which include dip and apparent dip,automatic identification is achieved. Testing the Gaussian kernel function with synthetic models and comparing the coherent attribute and dip attribute extraction results before and after,the conclusion that Gaussian filter can remove the random noise is obtained.
文摘Matrix factorization （MF） has been proved to be a very effective technique for collaborative filtering （ CF）, and hence has been widely adopted in today＇s recommender systems, Yet due to its lack of consideration of the users＇ and items＇ local structures, the recommendation accuracy is not fully satisfied. By taking the trusts among users＇ and between items＇ effect on rating information into consideration, trust-aware recommendation systems （TARS） made a relatively good performance. In this paper, a method of incorporating trust into MF was proposed by building user-based and item-based implicit trust network under different contexts and implementing two implicit trust-based context-aware MF （]TMF） models. Experimental results proved the effectiveness of the methods.
文摘协同过滤(CF)算法基于物品之间或用户之间的相似度能实现个性化推荐,然而CF算法普遍存在数据稀疏性的问题。针对用户‒物品评分稀疏问题,为使预测更加准确,提出一种基于协同训练与Boosting的协同过滤算法(CFCTB)。首先,利用协同训练将两种CF集成于一个框架,两种CF互相添加置信度高的伪标记样本到对方的训练集中,并利用Boosting加权训练数据辅助协同训练;其次,采用加权集成预测最终的用户评分,有效避免伪标记样本所产生的噪声累加,进一步提高推荐性能。实验结果表明,在4个公开数据集上,所提算法的准确率优于单模型;在稀疏度最高的CiaoDVD数据集上,与面向推荐系统的全局和局部核(GLocal-K)相比,所提算法的平均绝对误差(MAE)降低了4.737%;与ECoRec(Ensemble of Co-trained Recommenders)算法相比,所提算法的均方根误差(RMSE)降低了7.421%。以上结果验证了所提算法的有效性。
基金supported by the National Natural Science Foundation of China(No.61475118)the State Key Laboratory on Integrated Optoelectronics of China(No.IOSKL2015KF06)the National High-Tech Research and Development Program of China(No.2013AA014201)
文摘Aiming at the problem of the peak to average power ratio(PAPR)in coherent optical orthogonal frequency division multiplexing(CO-OFDM),a hybrid PAPR reduction technique of the CO-OFDM system by combining iterative partial transmit sequence(IPTS)scheme with modified clipping and filtering(MCF)is proposed.The simulation results show that at the complementary cumulative distribution function(CCDF)of 10^(-4),the PAPR of proposed scheme is optimized by 1.86 d B and 2.13 d B compared with those of IPTS and CF schemes,respectively.Meanwhile,when the bit error rate(BER)is 10^(-3),the optical signal to noise ratio(OSNR)are optimized by 1.57 dB and 0.66 d B compared with those of CF and IPTS-CF schemes,respectively.
