Nature inspired 21 branches sneezewort/Achillea ptarmica plant growth pattern–shaped antenna for Ku‐band applications

This reported paper presents the design and analysis of wideband nature inspired planar antenna of 46 × 18 × 1.6 mm³ size, designed using FR4 (Flame Retardant) substrate. The antenna patch is delineated from the growth pattern of sneezewort plant with supplementary notion of golden ratio in branching and branch width. The total number of branches in each stage follows the Fibonacci series and the branch width is calculated from the golden ratio concept. Starting from one feed line and two branches, the maximum numbers of branches are increased to 20. There has been significant enhancement in the radiation performance of proposed geometry by increasing the number of branches to 21 and by modifying ground with T‐shaped slot. The antenna covers wide impedance bandwidth of 8.2 to 16.5 GHz by possessing stable radiation characteristics.     

A low power and low phase-noise 91~96 GHz VCO in 90 nm CMOS

This paper reports a 94 GHz CMOS voltage-controlled oscillator (VCO) using both the negative capacitance (NC) technique and series-peaking output power and phase noise (PN) enhancement technique. NC is achieved by adding two variable LC networks to the source nodes of the active circuit of the VCO. NMOSFET varicaps are adopted as the required capacitors of the LC networks. In comparison with the conventional one, the proposed active circuit substantially decreases the input capacitance (C_in) to zero or even a negative value. This leads to operation (or oscillation) frequency (OF) increase and tuning range (TR) enhancement of the VCO. The VCO dissipates 8.3 mW at 1 V supply. The measured TR of the VCO is 91~96 GHz, close to the simulated (92.1~96.7 GHz) and the calculated one (92.2~98.2 GHz). In addition, at 1 MHz offset from 95.16 GHz, the VCO attains an excellent PN of – 98.3 dBc/Hz. This leads to a figure-of-merit (FOM) of ?188.5 dBc/Hz, a remarkable result for a V- or W-band CMOS VCO. The chip size of the VCO is 0.75 × 0.42 mm², i.e. 0.315 mm².     

风速时间序列混沌判定方法比较研究

混沌识别是对非线性时间序列进行混沌预测的前提。针对时间序列风速确定性与随机性相结合的复杂非线性特征,研究了不同的混沌识别方法,并对风速时间序列进行混沌特征识别。应用随机噪声、周期运动及经典混沌系统的时间序列对所选方法进行可靠性验证。对美国国家风能研究中心M2测风塔实测时间序列风速数据进行非线性混沌特征识别。结果表明:风速时间序列具有明显的混沌特征;各风速时间序列表现出不同程度的混沌特征;各混沌识别方法对风速时间序列混沌特征的表达形式不同,互为补充,相互验证。     

Investigation of seasonal variations of Beijing pavement condition data using unevenly spaced dynamic panel data model

Beijing municipal highway administration started to collect pavement condition data on its major expressways since 2006. It is advised in the Chinese practice standard that data collection shall be conducted on annual basis. However, pavement data are usually collected at different seasons of the year, which may cause significant seasonal variations in the observed condition. Moreover, for some reasons, data are missing for some of the pavement sections at certain years, which could bring up difficulties in performance model estimation and inference. These concerns have been simply neglected in past practice. This study proposed an unevenly spaced dynamic panel data model to investigate the seasonal patterns of a performance indicator called Ride Quality Index (RQI). A quasi-differencing approach was adopted for the estimation. Data collected from the 5th Ring Road of Beijing were used in the case study. It was found that RQI data collected during the fall season are expected to be lower than that collected during the spring or summer seasons. Findings from this research would be helpful to pavement engineers in using unevenly spaced pavement condition data for future condition estimation.     

融合环境因素的有限空间毒气扩散危害程度分析

为探究环境因素对毒气扩散危害程度的影响,通过对比有限空间内外环境差异,了解气体在有限空间内受力情况,并以传统高斯烟羽模型为基础,考虑扩散气体与有限空间边界碰撞产生的反射作用,建立了适用于有限空间气体扩散的改进高斯烟羽模型。深度剖析有限空间气体扩散环境,挖掘气体扩散主要关联因子,构建风速、地表粗糙度对有限空间气体扩散的影响函数;参照有限空间环境特征,分别设置3种不同风速与2种不同地表粗糙度,推求6种不同环境条件下气体扩散面积,阐明风速与地表粗糙度对有限空间气体扩散的影响程度。结果表明:在单一风速条件下,风速越高,地表粗糙度越低,洞室内H2S气体滞留时间越短,洞室内气体的扩散面积越小;风速越低,地表粗糙度越高,洞室内气体的扩散面积越大。     

Dual-PISA: An index for aggregation operations on time series data

Aggregation operations play an essential role in time series database management. As the number of data increases, it is difficult for current solutions, such as summary table and MapReduce-based methods to respond to such queries with low latency. Other approaches, such as segment tree-based methods, have a poor insertion performance when the data size exceeds the available memory. This paper proposes a Persistent Index for Segmented Aggregations (PISA), which has fast insertion performance and low latency for aggregation queries. PISA uses a forest to overcome the performance disadvantage of insertion in traditional segment trees. By defining two kinds of tags, namely code number and serial number, we propose an algorithm to accelerate queries by avoiding unnecessary reading data on disk. Additionally, we extend it to Dual-PISA to tolerate a range of unordered data, which is very important in the real world. Dual-PISA is stored on disk and is hugely memory-efficient — only takes a few hundred bytes of memory for billions of data points. Dual-PISA can be easily implemented on both traditional databases and NoSQL systems. It handles aggregation queries within milliseconds on a commodity server, for a time range that contains tens of billions of data points.     

A finite line source simulation model for geothermal systems with series- and parallel-connected boreholes and independent fluid loops

A model for the simulation of geothermal systems with parallel- and series-connected boreholes is presented. Mass and heat balance problems are formulated for each component in the system and are assembled into system-level problems. A third problem is formulated to account for heat transfer in the bore field, using the finite line source solution. This third problem is coupled to the system-level heat balance problem by an analytical solution of the heat transfer inside boreholes with multiple U-tubes. The simulation model allows for any number of independent fluid loops within the bore field or within individual boreholes and allows for combinations of specified inlet fluid temperatures and heat extraction rates in independent fluid loops. The model accounts for the axial variation of the fluid and borehole wall temperatures and heat extraction rates. The capabilities of the model are demonstrated through three example simulations.     

Weighted preliminary-summation-based principal component analysis for non-Gaussian processes

For industrial chemical process, preliminary-summation-based principal component analysis (PS-PCA), an amended PCA method was recently provided for coping with both Gaussian and non-Gaussian characteristics. By summing the training and monitoring data respectively, PS-PCA is capable of resolving the issue of non-Gaussian processes and achieves higher fault detection rate than the traditional PCA. However, in the PS-PCA summation operation, all data samples are regarded as the same weight, which results in the fault information of newly-samples may be diluted, leading to significant detection delays. To address this challenge, in this paper, we propose a novel weighted PS-PCA (WPS-PCA) method that employs an exponential weighting scheme to put more emphasis on recent information. Subsequently, a mathematical argument demonstrates that when the number of variables is enough plentiful, the obtained summation combined with the generalized central limit theorem conforms to approximately a Gaussian distribution. The kurtosis relationships indicate this conversion will bring out well-pleasing feasibility for conventional PCA. Ultimately, the proposed technique verifies detection performance using the Tennessee Eastman process, which is compared with the existing PCA and PS-PCA schemes, in terms of the fault detection time and fault detection rate. The simulation studies reveal that the proposed method is efficient and superior.     

Simultaneous identification of structural stiffness and mass parameters based on Bare-bones Gaussian Tree Seeds Algorithm using time-domain data

This paper mainly illustrates the Tree Seeds Algorithm (TSA) to tackle structural damage identification problem. The damage model is simulated by the alterations of both stiffness and mass parameters. The objective function is introduced by minimizing the differences between the measured and calculated acceleration data. To enhance the performance of the standard TSA, two modifications including the bare-bones Gaussian updated mechanism and the withering process are introduced. The modified algorithm is named after the BGTSA. In the numerical simulation part, the BGTSA is firstly used to make comparisons with several state-of-the-art algorithms on the CEC05. Secondly, the BGTSA is utilized to deal with the structural damage identification problem by optimizing the acceleration-based nonlinear objective function. Numerical experiments involving a simply supported beam and a truss are carried out to verify the effectiveness of the proposed algorithm. The final results show that with low amount of acceleration data, the BGTSA can acquire better identification results compared with other evolutionary algorithms. Therefore the proposed algorithm could be viewed as a potential tool to solve the structural damage identification problem.     

基于双树复小波的无参考立体图像质量评价

随着3D技术的不断发展，立体图像的使用领域越来越广泛，同时人们对图像的清晰度要求越来越高，因此，立体图像的质量评价成为关注点，基于此，提出了一种基于双树复小波变换的立体图像质量评价算法。使用双树复小波变换对立体图像的左、右视图进行处理，生成纹理结构图像，且根据最小能量误差的原理，获取左右视图的视差图；对纹理结构图像和视差图提取非对称广义高斯分布模型的参数、梯度幅值、相对梯度方向方差和奇异值曲线与坐标轴的面积等特征；使用AdaBoosting BP神经网络，进行训练和预测立体图像的质量得分。在LIVE立体图像数据库上的实验结果表明，新方法预测得分与主观得分有较好的一致性，获得了比较好的实验结果。