基于ARIMA与BP神经网络的收视率组合预测模型

针对收视率数据的非线性、突变性等特征,仅采纳单一的预测方法不能全面描述收视率的变化规律,因此提出了一种组合预测模型(ARIMA-BP).首先采用自回归移动平均模型(ARIMA)对收视率进行预测,提取收视率的线性变化规律,再利用BP神经网络对ARIMA模型的预测值进行进一步的组合预测,提取收视率的非线性变化特征.本文以广州电视综合频道及在广州市场上的其余8个电视频道19:00 ～20:00时间段的收视率为例进行实证分析,结果表明组合模型比单一采用ARIMA、BP神经网络进行预测的拟合效果好、精度高.     

基于GBDT算法的电视剧收视率预测

准确的收视率预测具有较高的商业价值,能够降低各方投资风险同时提高多方收益,形成合作共赢.为此,基于梯度提升决策树(Gradient Boosting Decision Tree,GBDT)算法建立电视剧收视率预测模型.研究表明,基于影响因素划分的GBDT电视剧收视率预测模型能够有效预测不同主创团队、题材及热度的电视剧的...     

浅谈城市电视频道的整体包装

我们过去谈电视包装,主要还是停留在制作几个电视频道形象宣传片、收视宣传片、节目的片头、片花等上面,而较少去整体考虑一个电视频道的整体包装。似乎有了这些宣传片,我们的电视包装就大功告成了,而实际上这只是电视包装工作的开始。电视频道的包装更重要的是在频道的整体推广设计及频道节目播出上——这也就是我们说的电视频道的“整体包装”。一个电视频道要有频道的形象宣传片,也要有频道呼号片花,对频道播出的节目要有片头,也要有节目宣传片。这是我们进行电视整体包装的基础,没有这些,我们的电视频道包装也就无从谈起。对于制作这些…     

基于改进WOA-Elman模型的舆情热度预测研究

针对公共卫生舆情事件的突发性和破坏性等问题,为了更精准预测舆情发生时的热度走向,本文构建了基于改进鲸鱼算法(WOA)优化Elman神经网络的舆情热度预测模型。首先根据百度指数和360趋势,对2020年1月1日至同年2月19日时段“COVID-19”事件的时间序列指标进行选取;其次利用WOA优化Elman神经网络初始值和阈值的方法进行训练和预测;最后与标准BP神经网络模型、标准Elman神经网络模型进行对比分析。结果表明,改进WOA-Elman的平均绝对百分比误差、均方根误差分别为4.784 3和219 363.784 4,该预测模型的预测结果与原始数据更吻合,预测精度和预测误差上更具优势,在解决突发公共卫生舆情事件热度预测问题上切实有效。     

网络设备工作负载综合预测研究

充分利用现有成熟预测模型的优势,取长补短,合理选取预测模型,结合负载影响因素,串联或者并联各模型输入或者输出,综合预测,进行网络设备动态负载预测.综合利用灰色和BP神经网络预测模型,并对其算法加以改进来提高预测准确度,分析并提出了一种综合预测模型,给出了具体的预测方法以及过程,并对预测结果进行分析,提高了预测精度,达到了预测目的.实际应用证明,该模型及方法在网络设备负载定量预测方面具有良好的效果,有较好的参考和使用意义.     

点击

截至2020年底,全国各级播出机构经批准开办高清电视和超高清电视频道756个,其中高清电视频道750个(包括开路高清频道696个、付费高清频道54个),4K超高清电视频道6个(包括开路超高清频道4个、付费超高清频道2个)。其中,中央广播电视总台已有22个高清频道和1个4K超高清频道,境内落地频道基本实现高清播出。除西藏卫视外,全国各省级台主频道均已实现高清播出。     

电视频道包装的核心内涵和品牌建设

通过对电视频道整体包装和品牌建设的关系再认识,具体分析了电视频道包装系统的核心内涵,从理念识别系统、视觉识别系统、行为识别系统入手,详细阐述了建立频道形象包装识别系统,打造优势品牌的具体内容,并指出电视品牌时代频道包装的个性化、人文化、内在化、规范化和时尚化的发展趋势.     

频道及栏目包装片制作的全新解决方案——新奥特敦煌视觉效果合成系统

各电视频道之间的竞争势必愈发激烈.而频道包装片作为频道整体形象的宣传载体,因其能够有效树立频道整体形象、彰显频道风格特点、提高频道品牌形象、吸引眼球提高收视率,而日益受到各家电视台的重视.本文介绍了敦煌视觉效果合成系统在天津滨海频道的应用设想举例.     

频道专业化定位之我见

电视频道专业化是指电视频道按照电视市场的内在规律和观众的特定需求 ,在现有电视频道资源的条件下 ,以整频道为单位进行重新定位划分 ,通常以某一类或几类节目作为频道的主体内容 ,以特定观众群为主要服务对象 ,使节目内容和频道风格能较集中地满足某些特定领域受众的需求 ,从而实现频道播出内容的专一化、收视对象的集中化、频道特色的明朗化。频道专业化的基本特征主要包括 :内容定位专一、制作人员专业、整体风格统一、市场营销一体、经营管理独立。频道专业化意味着频道中线性播出的是一系列内容相关、风格相近、收视对象趋同的节目集…     

从频道整体包装谈品牌建设——湖北电视综合频道整体形象升级改版

本文基于频道的核心理念,描述了频道理念识别、视觉识别和品牌推广的构建实践,分析了频道包装设计整体统一、规范完善的原则,得出了频道整体包装是频道品牌建设的基石、必须围绕着频道品牌建设进行设计推广、与频道品牌建设是一种共生互助关系之结论,频道包装设计与频道品牌建设共同服务于电视频道持续发展的终极目标.     

Long-range prediction of fading signals

It was previously proposed to adapt several transmission methods, including modulation, power control, channel coding, and antenna diversity to rapidly time variant fading channel conditions. Prediction of the channel coefficients several tens-to-hundreds of symbols ahead is essential to realize these methods in practice. We describe a novel adaptive long-range fading channel prediction algorithm (LRP) and its utilization with adaptive transmission methods. The LRP is validated for standard stationary fading models and tested with measured data and with data produced by our novel realistic physical channel model. Both numerical and simulation results show that long-range prediction makes adaptive transmission techniques feasible for mobile radio channels     

一种新型的基于深度学习的时变信道预测方法

针对高速移动正交频分复用系统,提出了一种新型的基于深度学习的时变信道预测方法。为了避免网络参数随机初始化造成的影响,本文方法首先基于数据与导频信息获取较理想的信道估计,利用其对BP神经网络进行预训练处理,以获取理想的网络初始参数;然后,基于预训练获取网络初始值,利用基于导频获取的信道估计对BP神经网络进行再次训练,以获取最终的信道预测网络模型;最后,本文方法基于该预测网络模型通过线上预测实现了时变信道的单时刻与多时刻预测。仿真结果表明,本文方法可以显著地提高时变信道预测精度,且具有较低的计算复杂度。     

北极冰水混合水域的水声信道预测技术

为在北极冰水混合水域环境下实现可靠的自适应水声通信,本文对此环境下的水声信道预测技术进行研究,利用水声信道的稀疏性,使用时域预测器对少数主要路径进行预测,降低计算复杂度的同时消除大部分噪声影响,提高预测精度。本文利用2018年第九次北极科学考察进行的水声通信试验数据比较了基于不同预测系数获取算法的信道预测方法性能,数据处理结果表明本文方法收敛后的信道预测误差可以达到-30dB,证明可以实现水声信道的有效预测,为冰水混合环境的水声通信提供可靠信道状态信息。      

Motion-compensating prediction with fractional-pel accuracy

The effect of fractional-pel accuracy on the efficiency of motion-compensating predictors is studied using various spatial prediction/interpolation filters. In model calculations, the power spectral density of the prediction error is related to the probability density function of the displacement error. Prediction can be improved both by higher accuracy of motion-compensation and by spatial Wiener filtering in the predictor. Beyond a critical accuracy, the possibility of further improving prediction by more accurate motion-compensation is small. Experiments with videophone signals and with broadcast TV signals confirm these model calculations. Sinc-interpolation, bilinear interpolation, and Wiener filtering are compared at integer-pel, 1/2-pel, 1/4-pel, and 1/8-pel accuracies. A three-state technique for reliable displacement estimation with fractional-pel accuracy is introduced. It is based on phase correlation. For motion-compensation with block size of 16 pels×16 pels, 1/4-pel accuracy appears to be sufficient for broadcast TV signals, whereas for videophone signals, 1/2-pel accuracy is desirable     

A performance bound for prediction of MIMO channels

Knowledge of future channel conditions can increase the performance of many types of wireless systems. This is especially true for radio channels with multiple transmit and receive antennas, i.e., multiple-input multiple-output (MIMO) systems. This paper derives a performance bound for MIMO channel prediction. It is assumed that prediction is based upon estimating a model for the channel and then extrapolating that model to predict future values of the channel. A vector formulation of the Crame/spl acute/r-Rao bound for functions of parameters is used to find a lower bound on the prediction error. Numerical evaluation of this bound shows that substantially longer prediction lengths are possible for MIMO channels than for single antenna channels. An intuitive interpretation of this result is that more of the channel structure is revealed when using multiple antennas at both ends. Finally, the longer prediction lengths for MIMO channels are confirmed by numerical results obtained by implementing a MIMO extension of a single-antenna prediction scheme.     

EDChannel: channel prediction of backscatter communication network based on encoder-decoder

Backscatter communication networks have attracted much attention due to their small size and low power waste, but their spectrum resources are very limited and are often affected by link bursts. Channel prediction is a method to effectively utilize the spectrum resources and improve communication quality. Most channel prediction methods have failed to consider both spatial and frequency diversity. Meanwhile, there are still deficiencies in the existing channel detection methods in terms of overhead and hardware dependency. For the above reasons, we design a sequence-to-sequence channel prediction scheme. Our scheme is designed with three modules. The channel prediction module uses an encoder-decoder based deep learning model (EDChannel) to predict the sequence of channel indicator measurements. The channel detection module decides whether to perform a channel detection by a trigger that reflects the prediction effect. The channel selection module performs channel selection based on the channel coefficients of the prediction results. We use a commercial reader to collect data in a real environment, and build an EDChannel model based on the deep learning module of Tensorflow and Keras. As a result, we have implemented the channel prediction module and completed the overall channel selection process. The experimental results show that the EDChannel algorithm has higher prediction accuracy than the previous state-of-the-art methods. The overall throughput of our scheme is improved by approximately 2.9% and 14.1% over Zhao’s scheme in both stable and unstable environments.

     

基于机器学习和卫星图像的路径损耗预测

基于反向传播神经网络（back propagation neural network,BPNN）构建了一种路径损耗预测模型. 通过卫星图像的红、绿、蓝（red, green and blue,RGB）通道的颜色信息来表征无线通信电波传播路径的环境特征,结合路测点与基站的距离特征构建数据集,迭代训练网络参数,以预测传播路径损耗. 结果表明,对跨基站路测点的预测结果与实测数据之间的相关系数达到0.83,绝对平均误差控制在0.66 dB,标准差控制在6.65 dB,说明在缺乏某一场景的详细模型和材质参数时,本文模型也能可靠预测无线通信电波的传播路径损耗. 此外,本文信道模型与传统信道建模方法多方面的对比与分析表明,本文模型在相同计算资源下可以提供和传统信道建模方法相差很小的预测结果,同时大大缩短预测所需的时间,说明本文模型对传播路径损耗做出快速预测的能力可以用于无线通信网络系统的优化.     

Particle filtering based autoregressive channel prediction model

A particle filtering based AutoRegressive (AR) channel prediction model is presented for cognitive radio systems. Firstly, this paper introduces the particle filtering and the system model. Secondly, the AR model of order p is used to approximate the flat Rayleigh fading channels; its stability is discussed, and an algorithm for solving the AR model parameters is also given. Finally, an AR channel prediction model based on particle filtering and second-order AR model is presented. Simulation results show that the performance of the proposed AR channel prediction model based on particle filtering is better than that of Kalman filtering.     

Low-calculation-cost fading channel prediction using chirp Z-transform

A low-complexity method to predict fast fading channels by estimating the parameters of the channel from the Doppler spectrum calculated using chirp Z-transform (CZT) is presented. The CZT fits the frequency-domain channel prediction for its ability to interpolate data points in the frequency domain, which reduces the frequency-quantisation error. Our evaluation results with time-division duplex show that the CZT method can significantly increase the channel prediction accuracy. The calculation cost of the CZT-based method is much lower compared to that of the zero-padding, autoregressive or high-resolution methods.     

Total variation regularization for fMRI-based prediction of behavior

While medical imaging typically provides massive amounts of data, the extraction of relevant information for predictive diagnosis remains a difficult challenge. Functional magnetic resonance imaging (fMRI) data, that provide an indirect measure of task-related or spontaneous neuronal activity, are classically analyzed in a mass-univariate procedure yielding statistical parametric maps. This analysis framework disregards some important principles of brain organization: population coding, distributed and overlapping representations. Multivariate pattern analysis, i.e., the prediction of behavioral variables from brain activation patterns better captures this structure. To cope with the high dimensionality of the data, the learning method has to be regularized. However, the spatial structure of the image is not taken into account in standard regularization methods, so that the extracted features are often hard to interpret. More informative and interpretable results can be obtained with the l(1) norm of the image gradient, also known as its total variation (TV), as regularization. We apply for the first time this method to fMRI data, and show that TV regularization is well suited to the purpose of brain mapping while being a powerful tool for brain decoding. Moreover, this article presents the first use of TV regularization for classification.