电气化铁路既有线深路堑扩堑石方控制爆破

介绍电气化铁路既有线无隔墙台阶爆破扩堑方法。沿既有线方向设置低台阶 ,边界处布置光爆孔 ,采用“同列同段和列间微差”的起爆网路。采用自制的“炮被”和架设“钢管排架”阻挡飞石、滚石和滑石 ,确保了既有线的安全。文中还概述了“炮被”和“钢管排架”的制做以及作者的认识和体会。     

Operational time-series data modeling via LSTM network integrating principal component analysis based on human experience

Today’s information technologies involve increasingly intelligent systems, which come at the cost of increasingly complex equipment. Modern monitoring systems collect multi-measuring-point and long-term data which make equipment health prediction a “big data” problem. It is difficult to extract information from such condition monitoring data to accurately estimate or predict health statuses. Deep learning is a powerful tool for big data processing that is widely utilized in image and speech recognition applications, and can also provide effective predictions in industrial processes. This paper proposes the Long Short-term Memory Integrating Principal Component Analysis based on Human Experience (HEPCA-LSTM), which uses operational time-series data for equipment health prognostics. Principal component analysis based on human experience is first conducted to extract condition parameters from the condition monitoring system. The long short-term memory (LSTM) framework is then constructed to predict the target status. Finally, a dynamic update of the prediction model with incoming data is performed at a certain interval to prevent any model misalignment caused by the drifting of relevant variables. The proposed model is validated on a practical case and found to outperform other prediction methods. It utilizes a powerful deep learning analysis method, the LSTM, to fully process big condition monitoring series data; it effectively extracts the features involved with human experience and takes dynamic updates into consideration.     

LTE系统多小区上行联合功控算法

针对传统的小区内开环功率控制算法通常以提升本小区的吞吐量性能为目标，忽略了当前小区用户对邻小区用户同频干扰的问题，为提升边缘用户性能的同时兼顾系统整体性能，提出了一种LTE系统小区间上行联合功率控制（UJPC）算法。该算法采用单基站三扇区为系统模型，以最优化系统吞吐量比例公平函数为目标，首先根据最小信干噪比（SINR）约束值和用户最大发射功率这两个约束条件得到相应的数学优化模型，然后采用连续凸近似的方法求解优化问题得出各个基站所管辖的小区内所有用户的最优发射功率。仿真结果表明，与基准的开环功控方案相比，联合功控方案在保证系统平均频谱利用率的情况下能够较大幅度地提高小区边缘频谱利用率，其最佳性能增益能达到50%。     

A note on calculating autocovariances of long-memory processes

In this paper, we consider a method (splitting) for calculating the auto- covariances of fractional integrated processes (ARFIMA) and generalized integrated processes (GARMA). The splitting method does not require any restriction on the autoregressive roots, and allows fast calculation of the autocovariances of these processes.     

Networks for sorting multitonic sequences

Lee and Batcher have designed networks that efficiently merge k separately provided sorted sequences of known lengths totalling n. We show that the design is still possible, and in fact easier to describe, if we do not make use of the lengths, or even the directions of monotonicity, of the individual sequences—the sequences can be provided in a single undelimited concatenation of length n. The depth of the simplest resulting network to sort sequences that are “k-tonic” and of length n is , generalizing Batcher's 1968 results for the extreme values of k (k=2 corresponding to merging, and k=n/2 corresponding to general sorting).The exposition is self-contained and can serve even as an introduction to sorting networks and Batcher's results.     

Machine-Learning based methods in short-term load forecasting

Short-term load forecasting is of great significance to the secure and efficient operation of power systems. However, loads can be affected by a variety of external impact factors and thus involve high levels of uncertainties. So it is a challenging task to achieve an accurate load forecast. This paper discusses three commonly-used machine-learning methods used for load forecasting, i.e., the support vector machine method, the random forest regression method, and the long short-term memory neural network method. The features and applications of these methods are analyzed and compared. By integrating the advantages of these methods, a fusion forecasting approach and a data preprocessing technique are proposed for improving the forecasting accuracy. A comparative study based on real load data is performed to verify that the proposed approach is capable of achieving a relatively higher forecasting accuracy.     

Consistent estimation of the memory parameter for nonlinear time series

Abstract. For linear processes, semiparametric estimation of the memory parameter, based on the log‐periodogram and local Whittle estimators, has been exhaustively examined and their properties well established. However, except for some specific cases, little is known about the estimation of the memory parameter for nonlinear processes. The purpose of this paper is to provide the general conditions under which the local Whittle estimator of the memory parameter of a stationary process is consistent and to examine its rate of convergence. We show that these conditions are satisfied for linear processes and a wide class of nonlinear models, among others, signal plus noise processes, nonlinear transforms of a Gaussian process ξ_t and exponential generalized autoregressive, conditionally heteroscedastic (EGARCH) models. Special cases where the estimator satisfies the central limit theorem are discussed. The finite‐sample performance of the estimator is investigated in a small Monte Carlo study.     

Transformations of residual fertilizer P in a semi-arid tropical soil under eight-year peanut-wheat rotation

Long-term transformations of residual phosphorus (P) governs the availability of phosphorus to crops. Very limited information is available on the transformations of residual fertilizer P in semi-arid tropical soils under long-term crop rotations. Therefore, using sequential phosphorus fractionation procedure, we studied changes in labile and stable forms of inorganic and organic P in a semi-arid alluvial soil (Typic Ustisamments) after eight years of annual fertilizer P application either to one crop (alternate) or to both crops (cumulative) in a peanut (Arachis hypogaea) — wheat (Triticum aestivum) rotation.Total residual fertilizer P in soil (P recovered from P-fertilized minus control plots) ranged from 62 to 176 mg P kg^–1. In the alternate P treatments (P applied to peanut or wheat only), on an average of 3 rates of applied P (13, 26 and 39 kg P ha^–1), in surface (0–15 cm) and subsurface (15 to 30 cm) soil, respectively, residual fertilizer P consisted of 14.8 and 2.2% resin-P, 8.6 and 2.8% NaHCO₃-P, 6.3 and 0% microbial-P, 31.4 and 4.2% NaOH-P, 7.8 and 3.0% aggregate protected-P, 12.5 and 3.0% HCl-P, 3.4 and 0% H₂SO₄-P. The corresponding values for surface and subsurface soils of cumulative P treatments (P applied to both peanut and wheat) were: 12.8 and 1.6% resin-P, 6.9 and 2.3% NaHCO₃-P, 4.7 and 0% microbial-P, 32.5 and 4.2% NaOH-P, 5.6 and 2.0% aggregate protected-P, 14.8 and 3.8% HCl-P, 6.7 and 2.1% H₂SO₄-P. Considerable lower values for the 15–30 cm depth indicate only a very small movement of residual P to the subsoil.Significantly lower amount of fertilizer P (28% and 44%) found in labile (resin, NaHCO₃ and microbial P) and semi-labile (NaOH and sonicated NaOH P) fractions for the cumulative P treatment than alternate P treatment (35 and 46%, respectively) suggests that increased rates and frequency of applied P tend to enhance the conversion of residual P to stable forms which are less available to plants. About 12 to 19% of residual fertilizer P found as organic P in labile and semi-labile forms confirmed that organic P increased with long-term fertilizer management. In conclusion, the results of our study suggest that the alternate application of fertilizer P to a crop, as is shown for wheat, helps reduce the transformations of residual P to stable P forms.     

SIMULATION AND ESTIMATION OF LONG MEMORY CONTINUOUS TIME MODELS

Abstract. Some general properties of long memory continuous time processes are recalled or proved. Methods of simulation are studied. A comparison with the usual discrete time autoregressive fractionally integrated moving-average filter is made and illustrations are provided. Then, two methods of estimation of the parameters of such a model from a discrete sample are studied, both theoretically and empirically, with Monte Carlo experiments.