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.     

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.     

基于VMD和改进TCN的短期光伏发电功率预测

光伏发电功率存在波动性,且光伏出力易受各种气象特征影响,传统TCN网络容易过度强化空间特性而弱化个体特性。针对上述问题,文中提出一种基于VMD和改进TCN的短期光伏发电功率预测模型。通过VMD将原始光伏发电功率时间序列分解为若干不同频率的模态分量,将各个模态分量以及相对应的气象数据输入至改进TCN网络进行建模学习。利用中心频率法确定VMD的最优分解模态分解个数。在传统TCN预测模型的基础上,使用DropBlock正则化取代Dropout正则化以达到抑制卷积层中信息协同的效果,并引入注意力机制自主挖掘并突出关键气象输入特征的影响,量化各气象因素对光伏发电的影响,从而提高预测精度。以江苏省某光伏电站真实数据为例进行仿真实验,结果表明所提预测方法的RMSE为0.62 MW,MAPE为2.03%。     

A mechanism informed neural network for predicting machining deformation of annular parts

Controlling machining deformation of annular parts is crucial for ensuring the performance of high value products and equipment. For example, during manufacturing of critical parts in aircrafts and spacecrafts, accurate prediction of machining deformation is the basis for guiding the formulation of deformation control strategies. However, due to the complexity of the machining deformation of annular parts, existing methods still have limitations in accurate prediction. To this end, this paper proposes a mechanism informed neural network (MINN) to predict machining deformation of annular parts. MINN is realized by establishing the dual sub-networks structure and using enhanced loss functions with the consideration of the deformation mechanism model characteristics of annular parts. The deformation was decomposed into the axisymmetric portion and the non-axisymmetric portion according to the deformation superposition principle, and modeled separately based on the thin-shell theory and Fourier series. Experiment results showed that the proposed method could predict the machining deformation of annular parts more accurately and stably with a small amount of training data, compared with previous methods.     

ChronoCorrelator: Enriching Events with Time Series

Event sequences and time series are widely recorded in many application domains; examples are stock market prices, electronic health records, server operation and performance logs. Common goals for recording are monitoring, root cause analysis and predictive analytics. Current analysis methods generally focus on the exploration of either event sequences or time series. However, deeper insights are gained by combining both. We present a visual analytics approach where users can explore both time series and event data simultaneously, combining visualization, automated methods and human interaction. We enable users to iteratively refine the visualization. Correlations between event sequences and time series can be found by means of an interactive algorithm, which also computes the presence of monotonic effects. We illustrate the effectiveness of our method by applying it to real world and synthetic data sets.