Predicting Prostephanus truncatus (Horn) (Coleoptera: Bostrichidae) populations and associated grain damage in smallholder farmers’ maize stores: A machine learning approach

Prostephanus truncatus is a notorious pest of stored-maize grain and its spread throughout sub-Saharan Africa has led to increased levels of grain storage losses. The current study developed models to predict the level of P. truncatus infestation and associated damage of maize grain in smallholder farmer stores. Data were gathered from grain storage trials conducted in Hwedza and Mbire districts of Zimbabwe and correlated with weather data for each site. Insect counts of P. truncatus and other common stored grain insect pests had a strong correlation with time of year with highest recorded numbers from January to May. Correlation analysis showed insect-generated grain dust from boring and feeding activity to be the best indicator of P. truncatus presence in stores (r = 0.70), while a moderate correlation (r = 0.48) was found between P. truncatus numbers and storage insect parasitic wasps, and grain damage levels significantly correlated with the presence of Tribolium castaneum (r = 0.60). Models were developed for predicting P. truncatus infestation and grain damage using parameter selection algorithms and decision-tree machine learning algorithms with 10-fold cross-validation. The P. truncatus population size prediction model performance was weak (r = 0.43) due to the complicated sampling and detection of the pest and eight-week long period between sampling events. The grain damage prediction model had a stronger correlation coefficient (r = 0.93) and is a good estimator for in situ stored grain insect damage. The models were developed for use under southern African climatic conditions and can be improved with more input data to create more precise models for building decision-support tools for smallholder maize-based production systems.     

基于材料力学性能的最低设计金属温度曲线分析

铁素体钢在低温条件下存在明显的韧脆转变现象。为防止脆断事故的发生,需要确定铁素体钢制压力容器的最低使用温度。针对ASME中的A^D四条冲击豁免曲线,对应地选取了4种材料,基于材料实际的屈服强度和参考温度,按照ASME中豁免曲线的计算方法,计算得到4种材料的最低设计金属温度曲线,并与对应的A^D曲线对比,定量地分析ASME中豁免曲线的保守性。结果表明,ASME中的豁免曲线相对于材料自身的最低设计金属温度曲线保守性较大;韧性相近的材料被划分到不同的豁免曲线,使得韧性富裕量差异较大,对于某些材料,其断裂韧性被低估。     

基于梯度提升决策树的车轮轮缘厚度磨耗预测

轮对在列车走行过程中起着导向、承受以及传递载荷的作用，其踏面及轮缘磨耗对地铁列车运行安全性和钢轨的寿命都将产生重要影响。根据地铁列车车轮磨耗机理，分析车轮尺寸数据特点，针对轮缘厚度这一型面参数，基于梯度提升决策树算法构建轮缘厚度磨耗预测模型。在该模型的基础上，任意选取某轮对数据进行验证分析，结果表明：基于梯度提升决策树的轮对磨耗预测模型具有较好的预测精度，可预测出1~6个月的轮缘厚度变化趋势范围，预测时间范围较长，可为地铁维保部门对轮对的维修方式由状态修转为预防修提供指导性建议。     

Mathematical representation of radiality constraint in distribution system reconfiguration problem

Distribution systems are most commonly operated in a radial configuration for a number of reasons. In order to impose radiality constraint in the optimal network reconfiguration problem, an efficient algorithm is introduced in this paper based on graph theory. The paper shows that the normally followed methods of imposing radiality constraint within a mixed-integer programming formulation of the reconfiguration problem may not be sufficient. The minimum-loss network reconfiguration problem is formulated using different ways to impose radiality constraint. It is shown, through simulations, that the formulated problem using the proposed method for representing radiality constraint can be solved more efficiently, as opposed to the previously proposed formulations. This results in up to 30% reduction in CPU time for the test systems used in this study.     

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.     

Polarity Effect and Electromagnetic Radiation of Partial Discharge Accompanying Growth of Electrical Tree

We have investigated the characteristics of radiated electromagnetic (EM) waves from positive and negative partial discharges (PD) in epoxy resin and cross‐linked polyethylene. We found that there is a correlation among the EM level from PD, the positive PD current, and electrical trees. Therefore, the growth of an electrical tree produces a lot of positive PD. We have also investigated the characteristics of the frequency region of EM waves from PD in air, insulating oil, and liquid epoxy in addition to the above insulators. EM waves were detected in the frequency region of 40 MHz to 300 MHz from positive and negative PD in epoxy resin and cross‐linked polyethylene. EM waves were also detected in the frequency region of 40 MHz to 150 MHz from positive and negative PD in air. In the case of insulating oil and liquid epoxy, EM waves were detected in the frequency regions of 40 MHz to 150 MHz from positive PD, and 40 MHz to 250 MHz from negative PD. The frequency region differed depending on the material and the discharge polarity. Our investigation indicates that the cause is differences in electric field strength at the time of PD occurrence.     

Reducing the complexity of distance measurement methods for circular turbo codes that use structured interleavers

The knowledge of turbo code's minimum Hamming distance (d_min) and its corresponding codeword multiplicity (A_min) is of a great importance because the error correction capability of a code is strongly tied to the values of d_min and A_min. Unfortunately, the computational complexity associated with the search for d_min and A_min can be very high, especially for a turbo code that has high d_min value. This paper introduces some useful properties of turbo codes that use structured interleavers together with circular encoding. These properties allow for a significant reduction of search space and thus reduce significantly the computational complexity associated with the determination of d_min and A_min values. © 2014 The Authors. International Journal of Communication Systems published by John Wiley & Sons, Ltd.     

A Reduced Reachability Tree for a Class of Unbounded Petri Nets

As a powerful analysis tool of Petri nets, reachability trees are fundamental for systematically investigating many characteristics such as boundedness, liveness and reversibility. This work proposes a method to generate a reachability tree, called ωRT for short, for a class of unbounded generalized nets called ω-independent nets based on new modified reachability trees (NMRTs). ωRT can effectively decrease the number of nodes by removing duplicate and ω-duplicate nodes in the tree, and verify properties such as reachability, liveness and deadlocks. Two examples are provided to show its superiority over NMRTs in terms of tree size.      

PMU based voltage security assessment of power systems exploiting principal component analysis and decision trees

The need for feature selection and dimension reduction is felt as a fundamental step in security assessment of large power systems in which the number of features representing the state of power grids dramatically increases. These large amounts of attributes are not proper to be used for computational intelligence (CI) techniques as inputs, because it may lead to a time consuming procedure with insufficient results and they are not suitable for on-line purposes and updates.This paper proposes a combined method for an online voltage security assessment in which the dimension of the token data from phasor measurement units (PMUs) is reduced by principal component analysis (PCA). Then, the features with different stability indices are put into several categories and feature selection is done by correlation analysis in each category. These selected features are then given to decision trees (DTs) for classification and security assessment of power systems.The method is applied to 39-bus test system and a part of Iran power grid. It is seen from the results that the DTs with reduced data have simpler splitting rules, better performance in saving time, reasonable DT error and they are more suitable for constant updates.