部分传输序列的遗传模拟退火搜索方法

在正交频分复用(OFDM)系统中,基于遗传算法的部分传输序列(GA-PTS)技术有效地降低了 PTS 的计算复杂度,但在 改进峰值-平均功率比(PAPR)性能方面却并不理想。 为此,提出在遗传算法中嵌入模拟退火(SA)算子从而构造一种混合的遗 传模拟退火(GSA)算法,并把它应用于对 PTS 的最优相位因子进行搜索。 首先,通过对 PTS 相位因子编码形成染色体,采用随 机元素组成的染色体作为遗传算法的初始群体,并评估每个染色体的适应度值。 然后,根据适应度值选择染色体,建立染色体 的变异规则和交叉规则,对群体进行迭代进化。 最后,群体中的染色体利用退火温度进行更新,从而产生出新的下一代种群。 仿真结果说明,与 GA-PTS 方案相比,该方法不仅能降低计算负担,而且能够有效地降低 OFDM 系统 PAPR 值。     

Optimal correlation of non-renewable and renewable generating systems for producing hydrogen and methane by power to gas process

This paper presents an efficient framework for converting renewable energy to gas and reducing Carbon dioxide (CO₂) footprint at the same time. The problem is presented in two levels. The first level is a minimization programming that minimizes operational cost and CO₂ of generators. The CO₂ is forwarded to the second level. In the second level, Carbon Capture and Storage (CCS) is designed to capture CO₂. The CO₂ is combined with Hydrogen and makes Methane (CH₄). The required Hydrogen is obtained from water electrolyzer that is supplied by the solar system. The capacity and size of water electrolyzer, solar system, and CCS is designed by the planning in the second level while this programming maximizes profit from selling Methane. As a result, the first level presents minimization programming (i.e., minimizing cost and CO₂) and the second level presents maximization programming (i.e., maximizing profit). The programming is developed taking into account solar uncertainty. The stochastic programming is implemented to cope with uncertainties. The problem is formulated as binary mixed integer linear programming and solved by GAMS software. The proposed power to gas (P2G) procedure efficiently designs proper solar system and deals with intermittency of solar energy, reduces CO₂ footprint, maximizes profit, and minimizes operational cost of generators at the same time.     

Time-frequency domain causality of prime building cost and macroeconomic indicators in Ghana: implications for project selection

According to the Ghana Statistical Service, construction activity generated US$3.8 billion in 2014, representing about 12.7% of nominal GDP. A key factor that most investors in the construction sector consider in project selection is cost which has escalated over the years. Therefore, we examine the co-movements and causality in the time and frequency domains between the prime building cost index (PBCI) and its disaggregates on one hand and inflation and exchange rate on the other hand in Ghana. The findings revealed that the degree of co-movement between PBCI and inflation was significant at the 5% level and occurred over the long-run, indicating that construction cost drives inflation. The degree of co-movement between the prime build cost index and exchange rate occurred within the short term. These co-movements have policy implications for substituting inflation and exchange rates in project costing by investors in Ghana. The co-movement between the PBCI and inflation in the medium to higher frequencies suggests that inflation risk-averse investors should consider short-term projects. Again, the short to medium frequency co-movements between the PBCI and exchange rate indicates that investors should consider exchange rate risk in long-term projects.     

变截面Z型折叠机翼振动特性的有限元与实验分析

Z型变截面折叠机翼作为一种可变体机翼结构,不同的折叠角对机翼稳定性有着重要的影响,因此研究不同折叠角度下的特性参数对机翼动态稳定性有着重要的意义.本文首先设计加工了Z型变截面可折叠机翼结构的实验模型,通过建立与实验模型相匹配的有限元模型,仿真得到不同折叠角度下机翼的前5阶固有频率和振型,针对不同折叠角度下机翼的固有特性,通过扫频实验得到机翼前5阶固有频率和模态振型,以及横向外激励作用下三段翼的频响曲线,对比分析有限元仿真与实验结果,验证结果的可靠性,这将对机翼结构设计以及特性参数的选取提供参考依据.     

热轧除鳞系统节能方案

针对热轧除鳞系统普遍存在的能源浪费严重的问题,设计了基于预测模型的变频节能方案,通过板坯位置预测、除鳞时间预测、除鳞水量预测和除鳞泵转速控制,合理利用轧制间歇时间使水泵电机变频降速,模拟计算结果表明,可使除鳞电耗降低23.57%。     

抗振条-传热管大间隙多跨直管束流致振动试验研究

针对抗振条-传热管大间隙的4跨传热管直管束开展了流致振动试验研究。传热管束转角正三角形排列，3处抗振条将直管束分为4跨，中间其中1跨的局部区域受到横向流体的冲刷。试验测试获得了管间流速在3.3～14.7 m/s区间内传热管振动位移和振动频率响应特性。结果表明，随着管间流速逐渐增大，传热管在来流方向和升力方向的振动频率依次增大，传热管的振动模态从抗振条1处有效支撑、2处未有效支撑的状态，转换为3处抗振条均有效支撑的状态。试验观测到传热管流弹失稳，其临界流速为14.5 m/s，与5种经验关系式预测结果的对比表明，Chen关系式能较好地预测流弹失稳的发生，预测结果较保守，与试验值间的相对偏差为21.4%。     

Gait event detection algorithm based on smart insoles

Gait analysis is an effective clinical tool across a wide range of applications. Recently, inertial measurement units have been extensively utilized for gait analysis. Effective gait analyses require good estimates of heel‐strike and toe‐off events. Previous studies have focused on the effective device position and type of triaxis direction to detect gait events. This study proposes an effective heel‐strike and toe‐off detection algorithm using a smart insole with inertial measurement units. This method detects heel‐strike and toe‐off events through a time‐frequency analysis by limiting the range. To assess its performance, gait data for seven healthy male subjects during walking and running were acquired. The proposed heel‐strike and toe‐off detection algorithm yielded the largest error of 0.03 seconds for running toe‐off events, and an average of 0–0.01 seconds for other gait tests. Novel gait analyses could be conducted without suffering from space limitations because gait parameters such as the cadence, stance phase time, swing phase time, single‐support time, and double‐support time can all be estimated using the proposed heel‐strike and toe‐off detection algorithm.     

脉冲电流对6016-T4铝合金板材力学性能的影响

为研究在电流辅助成形时脉冲电流的电流密度、占空比和脉冲频率3个因素对6016-T4铝合金板材力学性能的影响规律,设计了3因素5水平的正交实验。实验表明,对6016-T4铝合金力学性能影响的主次因素是:电流密度>占空比>脉冲频率。在此基础上,选用主次2个因素,即电流密度和脉冲频率,使用控制变量法进行进一步的实验研究。实验结果表明,电流密度对6016-T4铝合金力学性能影响较大,改变电流密度的同时带来了明显的焦耳热效应,该效应对材料有明显的软化作用,降低了材料的流动应力,但是,同时也降低了铝合金板的伸长率,这是因为过大的电流密度加剧了试样的主应变演化,进而促进了试样的断裂。而脉冲频率的变化对6016-T4铝合金板材的力学性能影响很小,这也印证了正交实验的结论。     

基于深度网络迁移学习的致痫区脑电识别* .txt

摘要：致痫区脑电识别能够为癫痫外科手术提供重要的参考价值。提出了一种基于深度网络迁移学习的致痫区脑电识别算法。首先利用连续小波变换（CWT）对脑电信号进行时频分析，获得脑电信号时频图；然后迁移学习AlexNet网络模型，调整网络结构使之适应于致痫区脑电识别，将模型第7层全连接层输出作为脑电信号时频图的特征表示，最后利用支持向量机（SVM）、BP神经网络、长短期记忆网络（LSTM）、基于稀疏表达分类算法（SRC）、线性判别分析（LDA）等分类算法进行特征分类。基于开源脑电数据集采用十折交叉验证的方法对算法进行了验证,比较6种分类器的效果，得到SVM算法的平均特异性为8881%，灵敏度为8807%，准确率为8844%，证明了该方法识别致痫区脑电信号的有效性。 .txt     

基于人工神经网络的柴油机失火故障诊断

车载诊断系统在诊断失火故障时,采用基于曲轴段角加速度和阈值规则相结合的方法,该方法在内燃机高速轻载运行时诊断单缸完全失火工况存在一定的局限性。通过对比分析失火和正常工况下曲轴瞬时转速的幅频和相频特征,提取不同谐次的幅值和相位信息,结合人工神经网络作为故障模式识别工具,得到了一种改善方法。通过台架实验,对此改善方法进行了单缸完全失火、两缸完全失火和单缸一定程度失火的故障诊断测试。结果表明,在实验条件下该方法可以有效识别不同的失火模式,并可在单缸失火模式下实现失火程度判别。同时,该方法通过少量工况数据训练神经网络,即可实现一定转速范围内的失火诊断,可行性强,可用于发动机失火故障在线诊断。