Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System

Higher transmission rate is one of the technological features of prominently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO–OFDM). One among an effective solution for channel estimation in wireless communication system, specifically in different environments is Deep Learning (DL) method. This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder (CNNAE) classifier for MIMO-OFDM systems. A CNNAE classifier is one among Deep Learning (DL) algorithm, in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from one another. Improved performances are achieved by using CNNAE based channel estimation, in which extension is done for channel selection as well as achieve enhanced performances numerically, when compared with conventional estimators in quite a lot of scenarios. Considering reduction in number of parameters involved and re-usability of weights, CNNAE based channel estimation is quite suitable and properly fits to the video signal. CNNAE classifier weights updation are done with minimized Signal to Noise Ratio (SNR), Bit Error Rate (BER) and Mean Square Error (MSE).     

基于SAOR的Massive MIMO系统信号检测算法

大规模多输入多输出(Massive multiple input multiple output, Massive MIMO)系统采用最小均方误差(Minimum mean square error, MMSE)接收检测方法时存在矩阵求逆复杂度高的问题，已有较多降低复杂度的研究。在降低检测算法复杂度的同时，如何提高算法收敛速度和检测性能一直是人们关注的焦点。本文将对称加速超松弛(Symmetric accelerated over-relaxation， SAOR)迭代算法应用于Massive MIMO系统信号检测中，避免了复杂的矩阵求逆计算，实现了复杂度较最小均方误差算法降低了一个数量级。仿真结果表明，基于SAOR的检测方法通过较少的迭代次数就能逼近最小均方误差（Minimum mean square error, MMSE）算法的检测性能，为Massive MIMO系统中接收信号的快速检测提供了较好的实现方法。     

基于DoG检测图像特征点的快速二进制描述子

针对SIFT描述子实时性差和传统二进制描述子对尺度、旋转和视角变化鲁棒性差的问题,本文通过优化采样模式和添加灰度差分不变量比较测试进行改进,提出了一种鲁棒性更高的二进制描述子。首先,设计了一种尺度关联、编号标记的采样模式;然后,旋转采样模式中各采样点到特定位置,确保描述子尺度、旋转不变性;接着,分析了采样点点对模式对描述子的影响,选择使用机器学习训练后的128对采样点对;最后,选择灰度值比较测试及梯度绝对值和比较测试构建二进制描述子。实验中采用DoG检测图像关键点,结果表明:本文提出的描述子在描述子构建和描述子匹配上比SIFT描述子分别快84%和67%;在有视角变化的图像匹配上,准确率比传统的二进制描述子高3%~5%,召回率平均要高30%以上。本文提出的特征点描述方法适用于时间要求高的图像匹配领域。     

A new linearly constrained minimum variance beamformer for reconstructing EEG sparse sources

Brain source imaging based on EEG aims to reconstruct the neural activities producing the scalp potentials. This includes solving the forward and inverse problems. The aim of the inverse problem is to estimate the activity of the brain sources based on the measured data and leadfield matrix computed in the forward step. Spatial filtering, also known as beamforming, is an inverse method that reconstructs the time course of the source at a particular location by weighting and linearly combining the sensor data. In this paper, we considered a temporal assumption related to the time course of the source, namely sparsity, in the Linearly Constrained Minimum Variance (LCMV) beamformer. This assumption sounds reasonable since not all brain sources are active all the time such as epileptic spikes and also some experimental protocols such as electrical stimulations of a peripheral nerve can be sparse in time. Developing the sparse beamformer is done by incorporating L1-norm regularization of the beamformer output in the relevant cost function while obtaining the filter weights. We called this new beamformer SParse LCMV (SP-LCMV). We compared the performance of the SP-LCMV with that of LCMV for both superficial and deep sources with different amplitudes using synthetic EEG signals. Also, we compared them in localization and reconstruction of sources underlying electric median nerve stimulation. Results show that the proposed sparse beamformer can enhance reconstruction of sparse sources especially in the case of sources with high amplitude spikes.     

铜矿床一般工业指标研究

针对2002年铜地质勘查规范中一般工业指标存在的问题,基于大量实际生产数据,对铜矿床实际采用的边界品位指标、最低工业品位、矿床平均品位、最小可采厚度、夹石剔除厚度、开采技术条件等进行研究,论证了铜矿床的一般工业指标,并首次明确了铜矿床工业指标的适用条件,解决了铜矿床一般工业指标体系应用不一致和指标区间取值主观性的问题。     

Regenerative braking energy recovery strategy based on Pontryagin's minimum principle for fell cell/supercapacitor hybrid locomotive

A regenerative braking energy recovery strategy based on pontryagin's minimum principle (PMP) for Fuel Cell (FC)/Supercapacitor (SC) hybrid power locomotive was proposed in this paper. In the proposed strategy, the dynamic coefficient λ is used in PMP during the traction state of the locomotive, which makes system transient hydrogen consumption minimum. What's more, during locomotives brake state, according to the known parameters of SCs and operation indicators, an optimized braking speed curve can also be obtained which has maximum brake recovery rate. The results are obtained from RT-LAB platform testify that the proposed strategy is able to maximize SC absorption braking energy, and the energy recovery rate improves and maintains SC state of charge (SoC) in a reasonable and safe range, and decreases brake resistors energy consumption in the braking process.     

Loadbearing capacity of infill masonry walls considering the deformation‐based membrane effect

According to currently valid codes, it is not possible to determine the loadbearing capacity of unreinforced infill walls considering the deformation‐based membrane effect by incorporating the exact support conditions. One reason for this is the lack of a validated calculation procedure, which in addition to the equilibrium conditions also realistically represents the compatibility conditions of these systems. In the present paper, therefore, a new non‐linear analytical calculation procedure is presented. The main focus of the analysis of walls subject to area loading is the incorporation of the support conditions and thus the consideration of the deformation‐based membrane compressive force. Through generalised formulation and a standardised notation of the determination equations, different material behaviours and various support conditions can be taken into account with few parameters. On the action side, both lighter loading like wind loads and heavier loading like explosion loads can be considered. Through the implementation of the partial factor concept, it is possible to comply with the requirements of European codes and thus ensure the applicability of the analysis model.     

基于最小方差的声线跟踪自适应分层算法

针对高精度水声定位系统中定位精度与计算量相互矛盾的问题,提出了基于最小方差的声线跟踪自适应分层算法。该算法根据声速在有限范围内规律变化的特点,对声速剖面全局搜索,通过各段声速散点到其拟合直线的总方差表示拟切分段与原始声速剖面的差别,利用最大差别找出最佳切分点,保留原始声速剖面特征,实现对声速剖面的简化。实验结果表明,该算法在保证定位精度的同时能够提高计算效率,具有良好的工程应用价值。     

降低CO2驱油最小混相压力化学体系研发

CO2驱提高原油采收率技术由于兼具高效、节能减排等优势在中外发展迅速,但胜利油田滩坝砂油藏CO2驱最小混相压力高,混相驱替难以进行,驱油效率较低。为此设计了一种兼顾增效和增溶作用的化学体系,可以显著降低CO2与原油之间的最小混相压力,改善非混相驱替效果。首先通过测定CO2中原油的抽提量及原油中CO2的溶解量,筛选出相应的增效剂DYJ-13和增溶剂S6。进一步对两种化学剂进行复配,系统考察了不同配比对CO2萃取抽提原油能力的影响,结果表明,随体系中DYJ-13质量分数的增加,增效因子先上升后下降,增溶因子变化不大,从而确定出最优的化学体系DYJ-13∶S6=3∶7。最后采用长细管驱替实验方法,测定了加入质量分数为3%的复配化学体系后,试验区原油与CO2之间最小混相压力由31.65 MPa降至24.60 MPa,降低幅度达22%。所研发的化学体系具有较高的应用潜力,建议开展单井试验。