高大建[构]筑物的共振震害

分析了国内外典型地震的共振震害个例。论述了致灾的长周期和大振幅地震波形成规律：①地震波周期随震级和震中距而增大;②长周期波出现与地基类型有关,软弱地基能传递长周期地震波,地基越软弱,越厚所能传递的波周期越长;③松软地基对地震波起放大作用,特别是表层松软、下层坚硬的地基,放大作用更显著,对高大建筑物危害更大。提出了减轻共振震害的对策。随着经济发展,高大建筑和结构物越来越多,潜在震害日益加重,研究共振震害对防震减灾具有现实意义。     

霍山窗与华东中强地震关系研究

分析了１９７２～１９９７年霍山窗小震的资料,研究了小震月频次、小震空间分布、Ｐ轴取向、Ｐ波初动等特征。认为当霍山窗小震月频次增高超过４０次时应注意３个月内华东地区在佛子岭东北或西南方向５００ｋｍ内可能发生中强地震。未来地震的大小、距离和方位与震群中的最大震级及佛子岭台记录到的Ｐ波初动符号的一致性和空间特征有关。     

砼强度的声速-衰减系数差值综合检测法

通过初步的理论分析,将一般的砼强度的声速－衰减系数综合检测法演变为声速－衰减系数差值综合检测法,对比试验证明该法较简单的Ｒ（Ｖ）法有明显的优点,从而使得该法由实验室研究走向现场应用成为可能。     

双波长分光光度法测定水中的钙

本文研究了均三溴偶氮胂(TBA)与钙的显色反应。结果表明,均三溴偶氮胂与钙在PH10.7的NH_4OH-NH_4Cl缓冲介质中形成1:2络合物,利用双波长分光光度法,以490nm为参比波长, 600nm为测定波长,测得ε490-600=3.1×10~4,钙量在0-30μg/25ml范围内符合比尔定律。本法用于自来水、河水、井水和锅炉水中钙的直接测定,结果满意。     

Grazing effects on sustainable semiarid rangeiands in Patagonia: The state and dynamics of the soil seed bank

The composition of the germinable seed bank was studied in four vegetation states of the Festuca pallescens grasslands in semiarid Patagonia during four years. The aim of this study was to test whether above-ground vegetation states resulting from grazing exclusion or different combinations of grazing and topography are reflected in different states of the germinable seed bank. The size of the total and dicot germinable seed bank was positively related to the total cover in each state. Dicots dominated all germinable seed bank states. Carex patagonica increased its cover as well as its germinable seed bank under grazing disturbance. Grazing did not reduce the germinable seed bank of perennial grasses in uplands where the grazing pressure is lower as compared with slopes. In slopes the germinable seed bank of perennial grasses was significantly reduced by grazing. A reduction of the length of the grazing period in late spring increases the germinable seed bank of perennial grasses both in upland and slope. These results are interpreted in the frame of a model of management techniques where grazing exclusion during late spring and late summer increases the seed bank of the perennial grasses and promotes their establishment in uplands. The artificial addition of seeds of perennial grasses and the manipulation of the soil surface in order to increase "safe sites" appear as management alternatives that deserve further evaluation to improve plant reestablishment in slopes.     

Effect of temperature tuning on the aerosol acoustic aggregation process

Diesel exhaust aerosols(DEAs) can absorb and accumulate toxic metal particulates and bacteria suspended in the atmospheric environment, which impact human health and the environment.The use of acoustic standing waves(ASWs) to aggregate DEA is currently considered to be an efficient particle removal method; however, study of the effect of different temperatures on the acoustic aggregation process is scarce. To explore the method and technology to regulate and optimize the aerosol aggregation process through temperature tuning, an acoustic apparatus integrated with a temperature regulation function was constructed. Using this apparatus, the effect of different characteristic temperatures(CTs) on the aerosol aggregation process was investigated experimentally in the ASW environment. Under constant conditions of acoustic frequency 1.286 kHz, voltage amplitude 17 V and input electric power 16.7 W, the study concentrated on temperature effects on the aggregation process in the CT range of 58–72°C. The DEA opacity was used. The results demonstrate that the aggregation process is quite sensitive to the CT, and that the optimal DEA aggregation can be achieved at 66°C. The aggregated particles of 68.17 μm are composed of small nanoparticles of 13.34–62.15 nm. At CTs higher and lower than 66°C, the apparatus in non-resonance mode reduces the DEA aggregation level. For other instruments, the method for obtaining the optimum temperature for acoustic agglomeration is universal. This preliminary demonstration shows that the use of acoustic technology to regulate the aerosol aggregation process through tuning the operating temperature is feasible and convenient.     

人群流动的波动性分析

在现代社会中,公共场所拥挤人群现象常常发生,人们对拥挤人群的机理研究较少,在公共场所因对拥挤人群的疏导或管理不当频频发生而伤亡事故。针对公共场所拥挤人群的安全管理需求,根据波动理论,特别是激波理论,来研究拥挤人群的基本特性,如密度、速度与激波的关系等。在人群拥挤时,可将其视为一连续介质,人群中产生的任何扰动(直接表现为密度的变化)都将以波的形式在人群中传播,同时由于人群中个体间的差异,导致波发生非线性畸变,最后可能导致激波的产生,即拥挤事故。通过特征值解法及Matlab软件,求解了对于不同的初始密度分布曲线、不同的速度,预测激波将在何时何处发生,即揭示激波产生的规律,总结了消除激波的一些措施,如改变边界条件、控制初始密度、设置畸点等,以避免拥挤事故的出现。     

液化石油气储罐设置方式之初探

从能量释放的角度出发,以爆炸理论为基础,研究了液化石油气储罐不同设置方式下发生爆炸的影响范围,并根据储罐三种设置方式的特点,给出了液化石油气储罐的合理设置方案.     

Hopkinson压杆反射拉伸下PMMA动态破坏现象实验研究

利用Hopkinson压杆系统对PMMA变截面柱锥试样进行冲击加载，研究材料动态拉伸断裂特性和破坏机理。由于PMMA的拉压强度差别不大，采用圆柱状试件难以产生反射拉伸破坏，因此，将试样设计为圆柱锥状，实验时，将试样的圆柱端粘贴于输入杆上。实验结果表明，在一定的撞击速度下，由于应力波的汇聚作用，在试样锥段产生了反射拉伸破坏；当撞击速度较高时，在产生反射拉伸破坏的同时，试件中还产生了银纹区，随着打击速度的提高，银纹区域增大。同时根据断口的SEM观察，研究了材料的破坏机制。     

Accuracy improvement of various short-term load forecasting models by a novel and unified statistical data-filtering method

ABSTRACT

Time-series and machine-learning methods are being strongly exploited to improve the accuracy of short-term load forecasting (STLF) results. In developing countries, power consumption behaviors could be suddenly changed by different customers, e.g. industrial customers, residential customers, so the load-demand dataset is often unstable. Therefore, reliability assessment of the load-demand dataset is obviously necessary for STLF models. Hence, this paper proposes a novel and unified statistical data-filtering method with the best confidence interval to eliminate unexpected noises/outliers of the input dataset before performing various short-term load forecasting models. This proposed novel data-filtering method, so-called the data pre-processing method, is also compared to other existing data-filtering methods (e.g. Kalman filter, Density-Based Spatial Clustering of Applications with Noise, Wavelet transform, and Singular Spectrum Analysis). By using an SCADA system?-based database of a typical 22kV distribution network in Vietnam, NYISO database, and PJM-RTO database, case studies of short-term load forecasting have been conducted with a conventional ARIMA model, an ANN forecasting model, an LSTM-RNN model, an LSTM-CNN combined model, a deep auto-encoder (DAE) network, a Wavenet-based model, a Wavenet and LSTM hybrid model, and a Wavelet Neural Network (WNN) model, which are to validate the novel and unified statistical data-filtering method proposed. The achieved numerical results demonstrate which the accuracy of the aforementioned STLF models can be significantly improved due to the proposed statistical data-filtering method with the best confidence interval of the input load dataset. The proposed statistical data-filtering method can considerably outperform the existing data-filtering methods.