LTE系统中速度估计新算法

在移动通信速度优化问题的研究中,终端速度的估计对长期演进(LTE)系统性能有重要的影响.针对传统信道自相关函数法在高速场景下应用受限的问题,提出了一种新的联合速度估计算法.上述算法在中速和低速场景下采用信道自相关函数法进行估计,在高速场景下采用频偏估计法进行估计,利用归一化接收功率标准差进行场景判断并在两种方法中切换,以提高速度估计精度.仿真结果表明,联合估计算法在低速、中速以及高速场景下均具有较高的估计精度,具有较好的应用价值.     

Experimental investigations of long stick wood gasification in a bottom lit updraft fixed bed gasifier

Based on the experience of the gasifier users on the efforts and energy for wood chip preparation in a typical gasifier, we have embarked on the development of a gasifier suitable to work with long stick woody biomass as the feed materials. In the context of the impact of gasifiers, as decentralized energy delivery devices such an approach, it is hoped, would be an attractive option in rural areas both in domestic and industrial sectors. In the present paper in the gasifier operation, there is a fixed quantity of char that is combusted to gasify a fixed amount of wood, and the gasifier does not operate in a steady state manner. In this present work, focus is made on the development of a gasifier using long sticks of wood as feed materials. With this concept, a 10 kW thermal output power gasifier is designed and constructed. The gas and airflows can be converted to the air/fuel ratio, the most important aspect of gasifier operation. The air/fuel ratio shows operation in a combustion mode at start up, a gasification mode for the middle part of the run and a charcoal gasification mode at the end of the run. Since the interest here is exploring and validating of this concept, a bottom lit updraft gasifier is designed mainly to look at the gas yield and other favourable factors and to use this gas so obtained for thermal applications. The rate of feed was between 9 and 10 kg/h and continuous operation for 5 h was made in a couple of runs to study the performance. In this paper we report the salient features of our efforts and results, yielding a gasifier efficiency of 73%.     

依靠技术攻关和创新创国内乙烯装置长周期运转新纪录

通过开展技术攻关,开发、采用在线反吹、"波动法"除垢、高效阻垢剂、在线检测与专家诊断系统等乙烯装置长周期运行技术,解决了裂解炉对流段超温、急冷油塔和裂解气压缩机结垢、乙炔前加氢反应器稳定性差等影响长周期运行的技术难题,实现了乙烯装置连续运转6年,创国内乙烯装置长周期运行新纪录,达到国际先进水平.     

Range Unit‐Root (RUR) Tests: Robust against Nonlinearities,Error Distributions,Structural Breaks and Outliers

Abstract. Since the seminal paper by Dickey and Fuller in 1979, unit‐root tests have conditioned the standard approaches to analysing time series with strong serial dependence in mean behaviour, the focus being placed on the detection of eventual unit roots in an autoregressive model fitted to the series. In this paper, we propose a completely different method to test for the type of long‐wave patterns observed not only in unit‐root time series but also in series following more complex data‐generating mechanisms. To this end, our testing device analyses the unit‐root persistence exhibited by the data while imposing very few constraints on the generating mechanism. We call our device the range unit‐root (RUR) test since it is constructed from the running ranges of the series from which we derive its limit distribution. These nonparametric statistics endow the test with a number of desirable properties, the invariance to monotonic transformations of the series and the robustness to the presence of important parameter shifts. Moreover, the RUR test outperforms the power of standard unit‐root tests on near‐unit‐root stationary time series; it is invariant with respect to the innovations distribution and asymptotically immune to noise. An extension of the RUR test, called the forward–backward range unit‐root (FB‐RUR) improves the check in the presence of additive outliers. Finally, we illustrate the performances of both range tests and their discrepancies with the Dickey–Fuller unit‐root test on exchange rate series.     

基于ViLBERT与BiLSTM的图像描述算法

传统图像描述算法存在提取图像特征利用不足、缺少上下文信息学习和训练参数过多的问题,提出基于ViLBERT和双层长短期记忆网络(BiLSTM)结合的图像描述算法.使用ViLBERT作为编码器,ViLBERT模型能将图片特征和描述文本信息通过联合注意力的方式进行结合,输出图像和文本的联合特征向量.解码器使用结合注意力机制的BiLSTM来生成图像描述.该算法在MSCOCO2014数据集进行训练和测试,实验评价标准BLEU-4和BLEU得分分别达到36.9和125.2,优于基于传统图像特征提取结合注意力机制图像描述算法.通过生成文本描述对比可看出,该算法生成的图像描述能够更细致地表述图片信息.     

基于卷积块注意力模块的图像描述生成模型

图像描述生成模型是使用自然语言描述图片的内容及其属性之间关系的算法模型.对现有模型描述质量不高、图片重要部分特征提取不足和模型过于复杂的问题进行了研究,提出了一种基于卷积块注意力机制模块(CBAM)的图像描述生成模型.该模型采用编码器-解码器结构,在特征提取网络Inception-v4中加入CBAM,并作为编码器提取图片的重要特征信息,将其送入解码器长短期记忆网络(LSTM)中,生成对应图片的描述语句.采用MSCOCO2014数据集中训练集和验证集进行训练和测试,使用多个评价准则评估模型的准确性.实验结果表明,改进后模型的评价准则得分优于其他模型,其中Model2实验能够更好地提取到图像特征,生成更加准确的描述.     

面向综合效益的城市充电网点规划研究

随着国家大力发展新能源汽车的政策,电动汽车和充电基础设施规模不断扩大,如何科学地规划充电网点成为亟待解决的问题。本文根据分期规划的原则,采用基于注意力机制的长短期记忆网络进行充电需求预测,使用量子粒子群优化算法进行近期充电网点选址定容,然后根据Voronoi图来划分远期充电站服务区域,从而建立了充电网点精准规划模型。通过对杭州市钱江世纪城区域进行验证,表明本文模型在科学规划充电网点的同时,能够实现电网、企业、用户的多方共赢。     

A novel deep learning prediction model for concrete dam displacements using interpretable mixed attention mechanism

Dam displacements can effectively reflect its operational status, and thus establishing a reliable displacement prediction model is important for dam health monitoring. The majority of the existing data-driven models, however, focus on static regression relationships, which cannot capture the long-term temporal dependencies and adaptively select the most relevant influencing factors to perform predictions. Moreover, the emerging modeling tools such as machine learning (ML) and deep learning (DL) are mostly black-box models, which makes their physical interpretation challenging and greatly limits their practical engineering applications. To address these issues, this paper proposes an interpretable mixed attention mechanism long short-term memory (MAM-LSTM) model based on an encoder-decoder architecture, which is formulated in two stages. In the encoder stage, a factor attention mechanism is developed to adaptively select the highly influential factors at each time step by referring to the previous hidden state. In the decoder stage, a temporal attention mechanism is introduced to properly extract the key time segments by identifying the relevant hidden states across all the time steps. For interpretation purpose, our emphasis is placed on the quantification and visualization of factor and temporal attention weights. Finally, the effectiveness of the proposed model is verified using monitoring data collected from a real-world dam, where its accuracy is compared to a classical statistical model, conventional ML models, and homogeneous DL models. The comparison demonstrates that the MAM-LSTM model outperforms the other models in most cases. Furthermore, the interpretation of global attention weights confirms the physical rationality of our attention-based model. This work addresses the research gap in interpretable artificial intelligence for dam displacement prediction and delivers a model with both high-accuracy and interpretability.     

基于深度残差长短记忆网络交通流量预测算法

针对多步交通流量预测任务中时间空间特征提取效果不佳和预测未来时间交通流量精度低的问题,提出一种基于长短时记忆（LSTM）网络、卷积残差网络和注意力机制的融合模型。首先,利用一种基于编解码器的架构,通过在编解码器中加入LSTM网络来挖掘不同尺度的时间域特征;其次,构建基于注意力机制挤压激励（SE）模块的卷积残差网络嵌入到LSTM网络结构中,从而挖掘交通流量数据中的空间域特征;最后,将编码器中获得的隐状态下的信息输入到解码器中,实现高精度多步交通流量的预测。基于真实交通数据进行实验测试和分析,实验结果表明,相较于原始的基于图卷积的模型,所提模型在北京和纽约两个交通流量公开数据集上的均方根误差（RMSE）分别获得了1.622和0.08的下降。所提模型能够高效且精确地对交通流量作出预测。     

基于注意力机制的多层次编码和解码的图像描述模型

图像描述任务是图像理解的一个重要分支,它不仅要求能够正确识别图像的内容,还要求能够生成在语法和语义上正确的句子。传统的基于编码器-解码器的模型不能充分利用图像特征并且解码方式单一。针对这些问题,提出一种基于注意力机制的多层次编码和解码的图像描述模型。首先使用Faster R-CNN（Faster Region-based Convolutional Neural Network）提取图像特征,然后采用Transformer提取图像的3种高层次特征,并利用金字塔型的融合方式对特征进行有效融合,最后构建3个长短期记忆（LSTM）网络对不同层次特征进行层次化解码。在解码部分,利用软注意力机制使得模型能够关注当前步骤所需要的重要信息。在MSCOCO大型数据集上进行实验,利用多种指标（BLEU、METEOR、ROUGE-L、CIDEr）对模型进行评价,该模型在指标BLEU-4、METEOR和CIDEr上相较于Recall（Recall what you see）模型分别提升了2.5个百分点、2.6个百分点和8.8个百分点;相较于HAF（Hierarchical Attention-based Fusion）模型分别提升了1.2个百分点、0.5个百分点和3.5个百分点。此外,通过可视化生成的描述语句可以看出,所提出模型所生成的描述语句能够准确反映图像内容。