Influencer-defaulter mutation-based optimization algorithms for predicting electricity prices

Efficient electricity price forecasting plays a significant role in our society. In this paper, a novel influencer-defaulter mutation (IDM) mutation operator has been proposed. The IDM operator has been combined with six well-known optimization algorithms to create mutated optimization algorithms whose performance has been tested on twenty-four standard benchmark functions. Further, the artificial neural network is integrated with mutated optimization algorithms to solve the electricity price prediction problem. The policymakers can identify appropriate variables based on the predicted prices to help future market planning. The statistical results prove the efficacy of the IDM operator on the recent optimization algorithms.     

浅析深孔爆破施工技术与成本控制的关系

以工程实践为基础,对深孔爆破的成本控制与施工方法、技术的关系进行了探讨。在不规则地形的情况下,有必要通过浅孔爆破进行平台的修正和根底的处理,使深孔台阶爆破处于良性循环中。把握好布孔、验孔、根底处理等主要工艺过程以及防止拒爆,是确保爆破安全、提高爆破质量和有效地控制成本的关键。     

A mathematical model for open pit mine production scheduling with Grade Engineering® and stockpiling

This paper presents the development and implementation of an innovative mixed integer programming based mathematical model for an open pit mining operation with Grade Engineering framework. Grade Engineering comprises a range of coarse-separation based pre-processing techniques that separate the desirable (i.e. high-grade) and undesirable (i.e. low-grade or uneconomic) materials and ensure the delivery of only selected quantity of high quality (or high-grade) material to energy, water, and cost-intensive processing plant. The model maximizes the net present value under a range of operational and processing constraints. Given that the proposed model is computationally complex, the authors employ a data pre-processing procedure and then evaluate the performance of the model at several practical instances using computation time, optimality gap, and the net present value as valid measures. In addition, a comparison of the proposed and traditional (without Grade Engineering) models reflects that the proposed model outperforms the traditional formulation.     

周期性隔震基础的优化设计与试验验证

周期性隔震基础是利用周期性结构的衰减域特性,将地震动与上部结构自振频率接近的频率范围内的能量衰减,从而控制上部结构响应的新型隔震装置。周期性基础的隔震性能受到多方面因素的影响,包括局域共振子的调谐频率比、质量比、阻尼比、基体的刚度、上部结构与基础的相互作用以及地震动的频谱特性等。目前尚未有研究综合考虑上述因素的影响并提出周期性基础的设计方法。提出以一组地震动作用下上部结构峰值响应衰减率的平均值为目标函数,对周期性基础进行优化的设计方法。计算结果表明,按照该优化方法得到的周期性基础对上部结构在选取的44条地震动作用下,峰值响应的平均衰减率达30%。参数敏感性分析表明,优化设计得到的周期性基础对于自身参数的变化具有良好的鲁棒性。最后利用缩尺振动台试验验证了优化设计得到的周期性基础的隔震性能,证明了优化方法的有效性和优化结果的正确性。     

Jaya Learning-Based Optimization for Optimal Sizing of Stand-Alone Photovoltaic,Wind Turbine,and Battery Systems

Renewable energy sources (RESs) are considered to be reliable and green electric power generation sources. Photovoltaics (PVs) and wind turbines (WTs) are used to provide electricity in remote areas. Optimal sizing of hybrid RESs is a vital challenge in a stand-alone environment. The meta-heuristic algorithms proposed in the past are dependent on algorithm-specific parameters for achieving an optimal solution. This paper proposes a hybrid algorithm of Jaya and a teaching–learning-based optimization (TLBO) named the JLBO algorithm for the optimal unit sizing of a PV–WT–battery hybrid system to satisfy the consumer’s load at minimal total annual cost (TAC). The reliability of the system is considered by a maximum allowable loss of power supply probability (LPSP_max) concept. The results obtained from the JLBO algorithm are compared with the original Jaya, TLBO, and genetic algorithms. The JLBO results show superior performance in terms of TAC, and the PV–WT–battery hybrid system is found to be the most economical scenario. This system provides a cost-effective solution for all proposed LPSP_max values as compared with PV–battery and WT–battery systems.     

山东栖霞—大柳行地区金矿找矿靶区地质评价及优选

栖霞—大柳行地区位于胶东中部栖霞—蓬莱金矿成矿带内，是胶东重要的金成矿区，成矿条件优越。为了对圈定靶区进行优选分类，通过对该区地质背景、找矿靶区评价、找矿靶区优选方面展开综合研究，并结合勘查工作程度和矿业权设置情况，划分出3类预测区，选定了5个靶区为今后优先部署勘查工作的方向。     

Selection of risk response actions with consideration of secondary risks

Secondary risk in project risk management refers to the risk that arises as a direct result of implementing a risk response action (RRA). It is important for project managers (PMs) to consider the effects caused by the secondary risks in the process of RRA selection. The purpose of this paper is to propose an optimization method to address the problem of selecting risk response actions (RRAs) with consideration of secondary risk which is seldom considered in the existing studies. The optimization model aims to minimize the total risk costs with time constraint being placed on the project makespan. By solving the model, an optimal set of RRAs along with the earliest start time for each activity can both be obtained. The results show that secondary risk plays an important role in the process of RRA selection. Project managers should allocate more budget for responding the project risk when the secondary risk is considered, and consider all factors relating to both time and cost so as to select appropriate RRAs to mitigate primary risk and secondary risk.     

Design optimization of semiconductor piezoresistors with Schottky diode contacts

A modeling theory is developed to predict the performance of piezoresistors which incorporate Schottky diode electrical contacts. This new theory allows the design of high performance gauges which can be fabricated using Non-Lithographically-Based Microfabrication (NLBM) techniques. These semiconductor piezoresistors can be designed in customizable sizes and fabricated in parallel in order to integrate position sensing into MEMS flexural positioners. Customizable sensing for nanopositioning platforms will enable advances in a range of nano-scale fabrication and metrology applications. A semiconductor piezoresistor with Schottky diode contacts was fabricated and attached to a titanium flexure. This device is shown to match predicted electrical performance within about 8% and to show a gauge factor of 116, within 2% of the predicted value. Optimized performance limits for Schottky diode semiconductor piezoresistors are identified to be about 127 dB full noise dynamic range for a quarter bridge over a 10 kHz sensor bandwidth on a 600 μm width titanium flexure, making them ideal for sensing on meso-/micro-scale flexural positioners. Methods are suggested for achieving the performance limits indicated above and the impact of these methods on the sensor dynamic range are studied.     

Identification of nonlinear Wiener-Hammerstein systems by a novel adaptive algorithm based on cost function framework

This paper investigates parameter identification of nonlinear Wiener-Hammerstein systems by using filter gain and novel cost function. Taking into account the system information is corrupted by noise, the filter gain is exploited to extract the system data. By using several auxiliary filtered variables, an extended estimation error vector is developed. Then, based on the discount term of the extended estimation error and the penalty term on the initial estimate, a novel cost function is developed to obtain the optimal parameter adaptive law. Compared with the conventional cost function which is composed of the square sum of output error, the proposed algorithm based on the cost function of this paper can provide faster convergence rate and higher estimation accuracy. Furthermore, the convergence analysis of the proposed scheme indicates that the parameter estimation error can converge to zero. The effectiveness and practicality of the proposed scheme are validated through the simulation example and experiment on the turntable servo system.     

Optimization control of hydrogen engine ignition system based on ACO-BP

With the development of new energy, hydrogen fuel engines have become a research boom in the automotive field. But there are abnormal problems such as backfire and pre-ignition during the combustion of hydrogen engines. This paper is based on the Ant Colony Optimization-Back Propagation (ACO-BP) algorithm to study the influence of different speed and load conditions on the ignition advance angle, so as to optimize the control of the hydrogen engine. The experimental system is established on a hydrogen engine converted from a 492Q gasoline engine. The prediction of the optimal ignition advance angle was obtained through experiments, and the optimal ignition MAP diagram of the hydrogen engine is constructed. The optimal ignition advance angle under different working conditions can effectively avoid the occurrence of hydrogen engine pre-ignition. The accuracy reaches 0.0018209 when the training reaches 14 times, the fitness between the actual value and the predicted value of ACO-BP training is 0.99921, the verification accuracy reaches 0.99913, and the test accuracy reaches 0.99932. Compared with the three optimization methods, the convergence speed and error accuracy of ACO-BP are significantly better than the BP neural networks and Genetic Algorithm-Back Propagation (GA-BP). This method realized the model of the nonlinear mapping model from hydrogen engine speed and load to optimal ignition advance angle, which is of great significance for solving the problem of abnormal combustion in hydrogen engine.