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.     

基于误差估值累加开环校正的诱导式欺骗检测方法

诱导式卫星欺骗干扰可诱导航空器逐渐偏离预定航迹，难以被发现，因此及时有效地检测干扰是飞行安全的保障。在现有紧组合导航体制基础上，设计了一种基于误差估值累加开环校正的紧组合导航结构，并证明了其性能与传统闭环校正紧组合导航性能等效。在此结构中，将紧组合导航系统与自适应序贯概率比检测方法结合，提出了一种基于误差估值累加开环校正的诱导式欺骗检测方法，融合紧组合导航信息与其他不受欺骗影响的导航信息，构建欺骗检测统计量进行诱导式欺骗检测。仿真结果表明，开环校正结构可避免随时间累加的惯性导航系统误差所导致的组合导航滤波器发散问题，同时欺骗检测方法可进一步提高算法对“最坏”情形下微小诱导式欺骗的检测效果。     

认知双向中继网络在κ-μ衰落信道下的中断性能分析

针对认知双向中继网络在进行数据传输时面临的复杂无线信道场景问题,采用广义κ-μ分布构建认知双向中继网络中的视距和非视距无线传输信道,推导次网络在κ-μ衰落信道下的统一中断概率,并分析次网络在多种单一和混合衰落信道情况下的中断性能。仿真结果表明,无线信道的衰落程度对次网络的中断概率影响显著,依据衰落信道类型合理设置网络参数将有助于提升次网络中断性能。     

基于多尺度的贝叶斯模型显著性检测

针对传统基于贝叶斯模型的显著性检测算法存在准确率不理想的问题,提出了一种基于多尺度的贝叶斯模型显著性检测算法。通过超像素分割算法(SLIC)将原图分割成不同尺度的超像素,根据超像素边界信息得到背景种子,进而通过距离计算和多尺度融合得到背景先验;对原图进行颜色增强,采用Harris算子对增强图进行检测角点求得凸包,融合不同尺度下的超像素得到凸包先验;融合背景先验和凸包先验得到最终先验;利用颜色直方图和凸包计算似然概率;将最终先验和似然概率通过贝叶斯模型计算显著图。在公开数据集MSRA1000、ECSSD上与多种传统算法进行准确率和召回率对比,该算法有更好的表现。     

Asynchronous control for two‐dimensional hidden Markovian jump systems with partly known mode observation conditional probabilities

This study focuses on the asynchronous control problem for two‐dimensional discrete‐time hidden Markovian jump systems where the mode observation conditional probability matrix is partly known. Considering the original system modes are invisible, the observed modes emitted from an observer serve as an alternative for stability analysis and controller design where a mode observation conditional probability matrix is constructed to characterize the emission between system modes and observed modes. Specially, only partly known information of the mode observation conditional probability matrix is accessible. With the introduction of the free‐connection weighting matrices, the asymptotic mean square stability criterion is firstly derived based on Lyapunov method. This introduction provides a further degree of relaxation and less conservatism is therefore achieved. Secondly, we present synthesis conditions for asynchronous state feedback controller design given in terms of a set of interconnected linear matrix inequalities. Moreover, cluster concept based on the partitions of observed modes is adopted which helps to decrease the number of controllers and simplify the design complexity. A numerical example, regarding the cases with and without clustering of the observed modes, is presented to illustrate the effectiveness of the proposed method.     

基于概率分析的核电传热管降质失效评估方法研究

发展核电是国家能源安全战略的重要组成部分，核电安全是对核电事业发展的最基本要求。依据核电传热管的检测历史数据和专家数据，利用全概率理论和Bayes公式提出了对传热管降质完整性评估的概率计算方法。通过对应力腐蚀、疲劳损伤等影响因素的分析，实现了在不同应力腐蚀和疲劳损伤条件下对传热管降质分布的概率计算。该评估方法可以将多个影响因素共同作用的复杂问题，简化为单个影响因素的逐一修正问题，简化了计算和处理方法。该方法可为核电蒸汽发生器传热管的降质概率评估提供借鉴。     

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.     

Time dependent failure probability estimation of the solid oxide fuel cell by a creep-damage related Weibull distribution model

In present paper, a new model is proposed and embedded into the finite element software ABAQUS to estimate the time dependent failure probability of the solid oxide fuel cell stack. The results show that sealant is the potential failure region of the solid oxide fuel cell stack, while the failure probability of the anode, electrolyte and cathode are very small within the operation time of 50,000 h. The creep and damage distribution of the components reflect that the proposed model can reasonably predict the time dependent failure probability of the solid oxide fuel cell stack. Increasing either the characteristic strain, Weibull modulus or decreasing the operating temperature can decrease the failure probability of the SOFC stack. For the sealant, to ensure the high temperature integrity of the SOFC stack, the characteristic strain should be larger than 0.01 or Weibull modulus should be higher than 8.0 under the operating temperature of 600 °C.     

Computing approximate blocking probabilities for transparent waveband switching based WDM networks using hierarchical cross-connects

An analytical model is presented to study the dynamics of wavelength division multiplexing (WDM) networks with waveband switching (WBS). The reduced load approximation method is considered to compute approximated network blocking probabilities in WBS-based WDM networks. The analytical model considers the link blocking probability due to insufficient link capacity and an impact of the waveband granularity (G). The analytical model also considers the node blocking probability due to unavailability of a switch port at the wavelength cross connect (WXC) layer of an Hierarchical cross connect (HXC) switch node. The set of nonlinear equations is obtained with the link independence assumption and solved using repeated substitutions. The accuracy of the analytical model is examined by comparing with simulation results considering the random-fit algorithm for waveband and wavelength assignments in different network scenarios. Lightpaths are routed between source and destination (s-d) HXC switch nodes using shortest path first (SPF) routing. An impact of the switch parameter to limit the input and the output WXC switch ports of an HXC switching node is also being investigated using the analytical model as well as through simulation results.     

盖子环替换及定点突变提高菜豆环氧化物水解酶对邻甲基苯基缩水甘油醚的催化性能

菜豆环氧化物水解酶1和2（PvEH1、PvEH2）能够动力学拆分外消旋邻甲基苯基缩水甘油醚（rac-oMGE），从而保留(R)-oMGE。基于对PvEH1和PvEH2结构的同源模拟和分析，发现二者分子中的盖子环差异较大，故本文选择盖子环作为研究目标。经融合聚合酶链式反应（FPCR），获得了PvEH2的盖子环区域被PvEH1对应区域替换的杂合酶Pv2Pv1。用全细胞酶E. coli/pv2pv1催化rac-oMGE，当(S)-oMGE刚好水解完全时，产物(S)-3-邻甲苯基-1,2-丙二醇（(S)-oTPD）的ee_p由PvEH2的58.3%提高至75.5%。为进一步提高酶的性质，在Pv2Pv1中选取11个氨基酸位点进行丙氨酸(A)突变，获得最优突变子E. coli/pv2pv1^K176A，活性为E. coli/pv2pv1（4.2U/g）的2.1倍，且当S构型的底物刚好完全水解时，(S)-oTPD的ee_p进一步提高为80.3%。分子对接分析发现，盖子环替换和K176位点突变为A，均使(R)-oMGE环氧环中的C_α更易受到酶中D101位点的攻击。利用E. coli/pv2pv1^K176A催化150mmol/L rac-oMGE水解制备(R)-oMGE（ee_s＞99%）和(S)-oTPD（ee_p=80.4%），二者的产率Y_S和Y_P分别为32.7%和60.1%，时空产率STY_S和STY_P为1.6g/(L·h)和3.3g/(L·h)。本实验为改善EH的催化性质提供了一种有效策略。