A gradient boosting decision tree approach for insider trading identification: An empirical model evaluation of China stock market

Insider trading is a kind of criminal behavior in stock market by using nonpublic information. In recent years, it has become the major illegal activity in China’s stock market. In this study, a combination approach of GBDT (Gradient Boosting Decision Tree) and DE (Differential Evolution) is proposed to identify insider trading activities by using data of relevant indicators. First, insider trading samples occurred from year 2007 to 2017 and corresponding non-insider trading samples are collected. Next, the proposed method is trained by the GBDT, and initial parameters of the GBDT are optimized by the DE. Finally, out-of-samples are classified by the trained GBDT–DE model and its performances are evaluated. The experiment results show that our proposed method performed the best for insider trading identification under time window length of ninety days, indicating the relevant indicators under 90-days time window length are relatively more useful. Additionally, under all three time window lengths, relative importance result shows that several indicators are consistently crucial for insider trading identification. Furthermore, the proposed approach significantly outperforms other benchmark methods, demonstrating that it could be applied as an intelligent system to improve identification accuracy and efficiency for insider trading regulation in China stock market.     

Modelling,simulation and identification of an engine air path electromechanical actuator

This paper deals with the modelling and the identification of an electromechanical Diesel engine actuator. The studied Bosch GPA-S actuator is designed for swirl/tumble flaps to control the air amount entering into the cylinder. This study aims to design a complete simulator that reproduces, with sufficient accuracy, the actuator dynamics taking into account the effects of the friction phenomenon. Hence, an overview of the actuator structure and its operation principle is first given. Then, its mathematical model as well as the nonlinearity, related to its behaviour, is discussed. Next, three identification procedures, which allow estimating both the system parameters and the friction model coefficients, are introduced. Finally, simulation results, using MATLAB, and experimental results, using LabVIEW, are presented demonstrating the effectiveness of the proposed techniques.     

Flow pattern identification and measurement techniques in gas-liquid-liquid three-phase flow: A review

The simultaneous flow of gas, oil, and water forms various flow patterns due to the complex interfacial relationships. Three-phase flow patterns are classified as the gas-liquid and liquid-liquid flow patterns. Pressure drop, void fraction, liquid holdup, and phase distribution are important characteristics of the three-phase flow. These characteristics are generally associated with the three-phase flow patterns. Hence, the knowledge about flow patterns can help to predict the overall behavior of the three-phase flow. Studies have been conducted to identify three-phase flow pattern and their characteristics at various superficial velocities of gas, oil, and water. The major purpose of the studies is to gather information about the three-phase co-current flow and use it for improvement of the efficiency of the flow systems. Therefore, the accuracy of the measurement technique is critical. Several types of flow pattern identification and measurement techniques have been developed to improve accuracy and provide high-quality results. In this article, classical and advanced techniques used for the three-phase flow identification and measurement have been reviewed. The survey will help the researchers working in the area of multiphase flow to choose the right technique based on the objectives of the studies.     

基于模糊判别的指纹识别算法探讨

指纹识别在社会各个领域应用都非常广泛.本文从指纹识别的原理和应用出发,介绍一种基于模糊判别的指纹识别算法.     

APPLICATION OF THE POLYMERASE CHAIN REACTION TO THE RAPID ANALYSIS OF BREWERY YEAST STRAINS

The rapid discrimination of closely-related Saccharomyces cerevisiae strains can pose a significant problem to breweries, in particular where closely related strains are being used simultaneously to manufacture different products. In this study, two PCR approaches have been examined to assess their usefulness for the discrimination of brewery ale and lager yeast strains. PCR using arbitrary primers (RAPD PCR) was found unsuitable for such an application since the DNA profiles generated from brewery strains were generally found to be identical, due presumably to the close genetic relatedness of these yeasts. In contrast, PCR using δ sequence primers could rapidly differentiate between many ale and lager strains and characteristic profiles for these were generated. This method could also be applied directly to yeasts isolated from brewery worts or from active dried yeast preparations. Results of such analyses were available within the working day.     

Identification of Chaos Representations of Elastic Properties of Random Media Using Experimental Vibration Tests

This paper deals with the experimental identification of the probabilistic representation of a random field modelling the Young modulus of a nonhomogeneous isotropic elastic medium by experimental vibration tests. Experimental data are constituted of frequency response functions on a given frequency band and for a set of observed degrees of freedom on the boundary of specimens. The random field representation is based on the polynomial chaos decomposition. The coefficients of the polynomial chaos are identified setting an inverse problem and then in solving an optimization problem related to the maximum likelihood principle.     

非线性系统Wiener模型辨识

本文采用不同幅值的三电平伪随机m序列作为输入信号,辨识了离散非线性系统Wiener 模型的线性脉冲响应函数和非线性增益系数.同时讨论了参数估计的统计特性及其区间估 计,并附有仿真结果.     

具有未知时滞的线性连续系统的辨识

本文给出了一种频域方法,它能用来辨识具有未知时滞的连续过程.     

Wiener models of direction-dependent dynamic systems

Analysis of systems with direction-dependent dynamics is currently limited to cases in which the dynamics in the two directions of the output are first order; results for such systems have been published for both pseudo-random maximum-length binary (MLB) and inverse-repeat maximum-length binary (IR-MLB) inputs. These relatively limited analytical results make it useful to examine alternative ways of modelling such systems and in this paper, Wiener models are considered for this purpose. Methods for optimising the Wiener model parameters by matching the system and model cross-correlation functions, outputs, and discrete Fourier transforms of the outputs are considered, and the results are compared. These methods are also applied to a first-order direction-dependent system with a maximum-length ternary (MLT) input, for which no analytical results are currently available, and to a second-order system with an IR-MLB input.     

Formation of Ketoacids in Ozonated Drinking Water

Three ketoacids; glyoxylic acid, pyruvic acid and ketomalonic acid, were identified in ozonated drinking waters and fulvic acid solutions using gas chromatography-mass spectrometry. It was found that the concentrations of ketoacids were much higher than those of aldehydes in ozonated waters. The significance of ketoacids in finished drinking waters is discussed.