Remark on the well-posedness of the problem of minimization of integral functionals

In this note, we study the well-posedness of the problem of constrained minimization of an integral functional and show that global well-posedness is equivalent to pointwise well-posedness almost everywhere. Our result extends an earlier one by Zolezzi.This research was supported by NSF Grant No. DMS 88-02688.     

Second-order numerical method for domain optimization problems

This paper is concerned with a second-order numerical method for shape optimization problems. The first variation and the second variation of the objective functional are derived. These variations are discretized by introducing a set of boundary-value problems in order to derive the second-order numerical method. The boundary-value problems are solved by the conventional finite-element method.The authors would like to express their thanks to Mr. T. Masanao, who was an undergraduate student, for his cooperation and comments. They also thank Professor Y. Sakawa of Osaka University for his encouragement.A part of this paper was presented at the IFIP Conference on Control of Boundaries and Stabilization, Clermont-Ferrand, France, 1988.     

求解不等式约束优化问题的一个改进算法

本文改进了一个求解不等式约束优化问题的对偶算法，建立了一个相应的算法，进一步证明了该算法的收敛性．最后，给出数值结果以验证该算法的有效性。     

基于可控提前期和服务水平约束的(Q,r)库存问题

本文研究了存在顾客流失情况下，基于可控提前期和服务水平约束的连续盘点（Q，r）库存管理同题。当市场需求分布未知时采用MiniMax方法建立年总成本函数表达式，修正以往研究中对安全系数取值范围错误判断，推导出合理的安全系数取值范围，并给出最优解的计算方法。     

Effects of initial states on the quantum correlations in the generalized Grover search algorithm

We investigate the correlations between two qubits in the Grover search algorithm with arbitrary initial states by numerical simulation. Using a set of suitable bases, we construct the reduced density matrix and give the numerical expression of correlations relating to the iterations. For different initial states, we obtain the concurrence and quantum discord compared with the success probability in the algorithm. The results show that the initial states affect the correlations and the limit point of the correlations in the searching process. However, the initial states do not influence the whole cyclical trend.     

Intelligent Fault Identification for Rolling Bearings Fusing Average Refined Composite Multiscale Dispersion Entropy-Assisted Feature Extraction and SVM with Multi-Strategy Enhanced Swarm Optimization

Rolling bearings act as key parts in many items of mechanical equipment and any abnormality will affect the normal operation of the entire apparatus. To diagnose the faults of rolling bearings effectively, a novel fault identification method is proposed by merging variational mode decomposition (VMD), average refined composite multiscale dispersion entropy (ARCMDE) and support vector machine (SVM) optimized by multistrategy enhanced swarm optimization in this paper. Firstly, the vibration signals are decomposed into different series of intrinsic mode functions (IMFs) based on VMD with the center frequency observation method. Subsequently, the proposed ARCMDE, fusing the superiorities of DE and average refined composite multiscale procedure, is employed to enhance the ability of the multiscale fault-feature extraction from the IMFs. Afterwards, grey wolf optimization (GWO), enhanced by multistrategy including levy flight, cosine factor and polynomial mutation strategies (LCPGWO), is proposed to optimize the penalty factor C and kernel parameter g of SVM. Then, the optimized SVM model is trained to identify the fault type of samples based on features extracted by ARCMDE. Finally, the application experiment and contrastive analysis verify the effectiveness of the proposed VMD-ARCMDE-LCPGWO-SVM method.     

Status Set Sequential Pattern Mining Considering Time Windows and Periodic Analysis of Patterns

The traditional sequential pattern mining method is carried out considering the whole time period and often ignores the sequential patterns that only occur in local time windows, as well as possible periodicity. Therefore, in order to overcome the limitations of traditional methods, this paper proposes status set sequential pattern mining with time windows (SSPMTW). In contrast to traditional methods, the item status is considered, and time windows, minimum confidence, minimum coverage, minimum factor set ratios and other constraints are added to mine more valuable rules in local time windows. The periodicity of these rules is also analyzed. According to the proposed method, this paper improves the Apriori algorithm, proposes the TW-Apriori algorithm, and explains the basic idea of the algorithm. Then, the feasibility, validity and efficiency of the proposed method and algorithm are verified by small-scale and large-scale examples. In a large-scale numerical example solution, the influence of various constraints on the mining results is analyzed. Finally, the solution results of SSPM and SSPMTW are compared and analyzed, and it is suggested that SSPMTW can excavate the laws existing in local time windows and analyze the periodicity of the laws, which solves the problem of SSPM ignoring the laws existing in local time windows and overcomes the limitations of traditional sequential pattern mining algorithms. In addition, the rules mined by SSPMTW reduce the entropy of the system.     

Modeling and Performance Optimization of an Irreversible Two-Stage Combined Thermal Brownian Heat Engine

Based on finite time thermodynamics, an irreversible combined thermal Brownian heat engine model is established in this paper. The model consists of two thermal Brownian heat engines which are operating in tandem with thermal contact with three heat reservoirs. The rates of heat transfer are finite between the heat engine and the reservoir. Considering the heat leakage and the losses caused by kinetic energy change of particles, the formulas of steady current, power output and efficiency are derived. The power output and efficiency of combined heat engine are smaller than that of single heat engine operating between reservoirs with same temperatures. When the potential filed is free from external load, the effects of asymmetry of the potential, barrier height and heat leakage on the performance of the combined heat engine are analyzed. When the potential field is free from external load, the effects of basic design parameters on the performance of the combined heat engine are analyzed. The optimal power and efficiency are obtained by optimizing the barrier heights of two heat engines. The optimal working regions are obtained. There is optimal temperature ratio which maximize the overall power output or efficiency. When the potential filed is subjected to external load, effect of external load is analyzed. The steady current decreases versus external load; the power output and efficiency are monotonically increasing versus external load.     

激光诱导荧光光谱快速检测食源性致病菌

近年来,由微生物污染引起的食品安全问题对人类健康构成威胁。微生物的快速检测对食品安全具有重要意义。目前,微生物快速检测技术存在操作困难,成本高的不足。激光诱导荧光光谱(LIFS)具有灵敏度高、操作方便、设备相对便宜等优点,为微生物的快速检测提供了一种潜在技术。利用便携式405 nm激光激发三种常见食源性致病菌(粪肠球菌、鼠伤寒沙门氏菌和铜绿假单胞菌)的荧光,并利用微光纤光谱仪检测光谱。通过调节激光器功率(10～100 mW)得到粪肠球菌的荧光强度,验证了激光器功率(Power,P)与细菌荧光强度的关系,结果表明最佳激光器功率范围为50～80 mW。测量了在激光器功率P=50 mW时细菌样品的荧光光谱,并讨论了细菌种类和荧光光谱之间关系。结合文献分析粪肠球菌在528 nm处出现黄酮的荧光峰,铜绿假单胞菌中的原卟啉发射634 nm荧光峰。实验结果表明：(1)铜绿假单胞菌在634和703 nm处的荧光峰,可作为直接识别特征;(2)基于多元统计,将粪肠球菌和鼠伤寒沙门氏菌的光谱划分为9个特征区,采用动态聚类法得到粪肠球菌和鼠伤寒沙门氏菌的识别率均达到100%。结果表明,激光诱导荧光光谱法可有效检测铜绿假单胞菌、粪肠球菌和鼠伤寒沙门氏菌。相较于其他微生物快速检测技术,LIFS方法操作方便,检测速度快,识别率高,对食源性致病菌的快速检测具有重要的应用价值。     

基于TDLAS技术的CH4气体检测与温度补偿方法

CH₄气体的精准检测对防止矿井瓦斯爆炸,确保安全生产至关重要。目前基于可调谐半导体激光吸收光谱技术（TDLAS）存在因温度变化导致气体浓度测量误差较大。探究了基于TDLAS的CH₄气体检测系统与温度补偿方法,分析温度对CH₄气体吸收谱线的影响,通过算法补偿模型消除环境温度对CH₄气体检测的影响。依据TDLAS技术原理及相关理论,对系统发射单元、吸收池、信号接收单元、数据处理单元进行设计,搭建了基于TDLAS技术的CH₄气体浓度检测系统,实验检测了不同环境温度（10~50 ℃）时0.04%CH₄气体浓度,分析温度变化对CH₄气体在波长为1.653 μm处吸收谱线强度和半宽度的影响。为消除温度对CH₄气体检测的影响并提高补偿效果,采用粒子群优化算法（PSO）优化BP神经网络（BPNN）的最佳权值和阈值,建立CH₄气体的PSO-BP温度补偿模型,克服了BP神经网络收敛速度慢、易陷入局部最优的缺点。结果表明：（1）基于TDLAS的CH₄气体检测浓度随环境温度升高而下降,整个实验温度内相对误差范围为4.25%~12.13%,不同环境温度下CH₄气体检测浓度与温度之间的关系可用一元三次多项式表示;（2）CH₄气体的吸收强度和半宽度随着温度的升高而下降,与温度变化之间的关系为单调递减函数,温度对CH₄气体吸收谱线强度的相对变化率大于吸收谱线半宽度的相对变化率,CH₄气体吸收谱线的强度更容易受温度变化的影响;（3）BP神经网络和PSO-BP模型测试样本的绝对平均误差（MAE）分别为12.88%和1.81%,平均绝对百分比误差（MAPE）分别为2.3%和0.3%,均方根误差（RMSE）分别为15.96%和2.69%,相关系数R2分别为0.980 6和0.999 6。通过建立PSO-BP温度补偿模型,补偿效果大部分分布在±1.0%的误差范围内,MAE,MAPE,RMSE和R2等评价指标均大幅度提升,对提高TDLAS技术在矿井CH₄的精准检测具有一定的参考意义。