考虑功率预测的储能电站动态优化控制方法

提出了基于超短期功率预测的储能电站平抑风电功率波动的动态优化控制方法,系统考虑影响储能电站的多种因素,构建基于风功率超前信息的充放电策略;以荷电状态偏移量最小为目标构建优化控制模型,同时考虑储能介质的性能约束,得到下一次控制步长内的优化控制模式,由此形成递进式的动态控制策略。实际风电场运行数据算例分析表明,所提方法可有效实现储能电站的高效控制,具有一定实际应用价值。     

人工神经网络预测刀具磨损和切削力

刀具磨损和切削力预测与控制是切削加工过程中需要考虑的重要问题.本文介绍了利用人工神经网络模型预测刀具磨损和切削力的步骤并且针对产生误差的因素进行分析.首先将切削速度、切削深度、切削时间、主轴转速和不同频带的能量值通过归一化法处理,作为输入特征值,对改进的神经网络模型进行训练.然后利用训练完成的神经网络模型预测刀具磨损和切削力.结果表明:神经网络模型能够综合考虑加工过程中更多的影响因素,与经验公式结果对比,具有更高的预测精度.研究结果表明神经网络模型预测刀具磨损和切削力具有可行性和准确性,为刀具结构的优化及加工参数的选择提供了依据.     

Diagnostics and forecasting of cutting tool wear at CNC machines

The problem of monitoring and forecasting the remaining cutting tool durability is formulated and an architectural model of a generalized diagnostic system and its software implementation are suggested. A diagnostic module/CNC system kernel protocol is specified and a universal solution to diagnosing and predicting cutting tool wear is presented being based on an external calculator.     

基于自适应混合优化的电力数据预测方法

电力数据易受气候、季节、节假日等因素影响,出现不同波动特征.针对不同特征电力数据预测精度不高、预测方法泛化能力弱等问题,提出基于自适应混合优化的电力数据预测方法 .通过使用小波变换和平稳性分析,将电力数据自适应地分解为包含趋势、季节和周期信息的非平稳序列和多个平稳序列;使用状态转移算法分别优化长短时记忆深度学习网络和自回归移动平均模型,对非平稳序列和平稳序列分别拟合、预测;对预测的各序列进行重构,得到最终预测结果.在电力系统数据上进行多步预测,对比实验表明:与其他方法相比,所提方法不仅具有更高的预测精度,还具有较强的泛化能力.     

基于元胞粒子群算法的数控切削参数优化

针对数控切削参数优化问题的非线性和多约束性质,采用一种元胞粒子群算法（CPSO）进行优化。在基本粒子群算法（PSO）思想的基础上,引入邻居的概念,以搜索解空间的局部信息,并将粒子的信息交流范围扩展到种群外部,从而能搜索到更有希望的解空间;在罚函数机制的基础上,引入标志变量记录粒子是否曾经满足过所有约束条件,根据标志变量进行粒子个体极值与种群全局极值的更新。通过比较CPSO算法与其他算法取得的结果,验证该算法解决数控切削参数优化问题的有效性和优越性。     

A dynamic optimization approach for adaptive incremental learning

A fundamental problem when performing incremental learning is that the best set of a classification system's parameters can change with the evolution of the data. Consequently, unless the system self‐adapts to such changes, it will become obsolete, even if the application environment seems to be static. To address this problem, we propose a dynamic optimization approach in this paper that performs incremental learning in an adaptive fashion by tracking, evolving, and combining optimum hypotheses overtime. The approach incorporates various theories, such as dynamic particle swarm optimization, incremental support vector machine classifiers, change detection, and dynamic ensemble selection based on classifiers' confidence levels. Experiments carried out on synthetic and real‐world databases demonstrate that the proposed approach actually outperforms the classification methods often used in incremental learning scenarios. © 2011 Wiley Periodicals, Inc.     

自适应调整参数的果蝇优化算法

针对基本果蝇优化算法因参数选取不当而导致的收敛精度偏低且不稳定的问题,提出了自适应调整参数的果蝇优化算法（FOA with Adaptive Parameter,FOAAP）。该算法在每个进化代输入描述种群整体特征的精确数值,由逆向云发生器算法得到当代云模型的3个数字特征[C(Ext,Ent,Het)],按照[U]条件隶属云发生器自适应调整果蝇个体搜寻食物的方向与距离[Value]这一参数。将该算法在函数优化中,与基本果蝇优化算法以及相关文献中算法进行仿真对比,结果表明,新算法在收敛速度、收敛可靠性及收敛精度方面具有明显优势。     

利用T-S模糊自适应PSO算法优化PID参数

针对微粒群优化算法存在的早熟问题,提出了一种基于T-S模型的模糊自适应PSO算法（T-SPSO算法）。算法依据种群当前最优性能指标和惯性权重值所制定T-S规则,动态自适应惯性权重取值,改善了PSO算法的收敛性。将该算法应用于PID控制器的参数整定,可得到更优的控制器参数。仿真结果验证了所提出算法的有效性和所设计控制器的优越性。     

Tool wear monitoring and selection of optimum cutting conditions with progressive tool wear effect and input uncertainties

One of the big challenges in machining is replacing the cutting tool at the right time. Carrying on the process with a dull tool may degrade the product quality. However, it may be unnecessary to change the cutting tool if it is still capable of continuing the cutting operation. Both of these cases could increase the production cost. Therefore, an effective tool condition monitoring system may reduce production cost and increase productivity. This paper presents a neural network based sensor fusion model for a tool wear monitoring system in turning operations. A wavelet packet tree approach was used for the analysis of the acquired signals, namely cutting strains in tool holder and motor current, and the extraction of wear-sensitive features. Once a list of possible features had been extracted, the dimension of the input feature space was reduced using principal component analysis. Novel strategies, such as the robustness of the developed ANN models against uncertainty in the input data, and the integration of the monitoring information to an optimization system in order to utilize the progressive tool wear information for selecting the optimum cutting conditions, are proposed and validated in manual turning operations. The approach is simple and flexible enough for online implementation.     

一种参数优化的图像滤波方法研究

摘 要;在频域滤波的理论基础上,提出了一种基于参数的巴特沃斯高通和低通相结合的图像滤波方法,并用MATLAB软件进行了实验,结果表明,对于去除噪声、提取目标信息及图像增强等,该算法显示出了较好的效果.     

基于自适应粒子群优化算法的测试数据扩增方法

针对在回归测试中原有测试数据集往往难以满足新版本软件测试需求的问题,提出一种基于自适应粒子群算法（APSO）的测试数据扩增方法。首先,根据原有测试数据在新版本程序上的穿越路径与目标路径的相似度,在原有的测试数据集中选择合适的测试数据,作为初始种群的进化个体;然后,利用初始测试数据的穿越路径与目标路径的不同子路径,确定造成两者路径偏离的输入分量;最后,根据路径相似度构建适应度函数,利用APSO操作输入分量,生成新的测试数据。该方法针对四个基准程序与基于遗传算法（GA）和随机法的测试数据扩增方法相比,测试数据扩增效率分别平均提高了约56%和81%。实验结果表明,所提方法在回归测试方面有效地提高了测试数据扩增的效率,增强了其稳定性。     

An advanced numerical approach on tool wear simulation for tool and process design in metal cutting

Tool wear is an important criterion in metal cutting affecting part quality, chip formation and the economics of the cutting process. In order to account for tool wear adequately in tool and process design, simulation tools predicting tool wear in metal cutting processes are required. Within this paper, an advanced simulation approach is presented, coupling FE simulations of chip formation with a user-defined subroutine which extends the functionalities of the commercial FE code for wear simulation laying the focus on the development of this method. The continuous process of wearing is discretized in finite steps and the wear rate is modelled to be constant between. Based on the Usui wear rate equation, the local thermo-mechanical load obtained by FE simulation is transformed into local wear rates. The geometric representation of the wear progress is implemented via shifting of the finite element nodes of the engaged tool domain. A novel iterative procedure of updating the tool geometry in order to account for the wear progress is presented.     

Digital twin-driven surface roughness prediction and process parameter adaptive optimization

In the process of parts machining, the real-time state of equipment such as tool wear will change dynamically with the cutting process, and then affect the surface roughness of parts. The traditional process parameter optimization method is difficult to take into account the uncertain factors in the machining process, and cannot meet the requirements of real-time and predictability of process parameter optimization in intelligent manufacturing. To solve this problem, a digital twin-driven surface roughness prediction and process parameter adaptive optimization method is proposed. Firstly, a digital twin containing machining elements is constructed to monitor the machining process in real-time and serve as a data source for process parameter optimization; Then IPSO-GRNN (Improved Particle Swarm Optimization-Generalized Regression Neural Networks) prediction model is constructed to realize tool wear prediction and surface roughness prediction based on data; Finally, when the surface roughness predicted based on the real-time data fails to meet the processing requirements, the digital twin system will warn and perform adaptive optimization of cutting parameters based on the currently predicted tool wear. Through the development of a process-optimized digital twin system and a large number of cutting tests, the effectiveness and advancement of the method proposed in this paper are verified. The organic combination of real-time monitoring, accurate prediction, and optimization decision-making in the machining process is realized which solves the problem of inconsistency between quality and efficiency of the machining process.     

An adaptive optimization technique for dynamic environments

The use of evolutionary algorithms (EAs) is beneficial for addressing optimization problems in dynamic environments. The objective function for such problems changes continually; thus, the optimal solutions likewise change. Such dynamic changes pose challenges to EAs due to the poor adaptability of EAs once they have converged. However, appropriate preservation of a sufficient level of individual diversity may help to increase the adaptive search capability of EAs. This paper proposes an EA-based Adaptive Dynamic OPtimization Technique (ADOPT) for solving time-dependent optimization problems. The purpose of this approach is to identify the current optimal solution as well as a set of alternatives that is not only widespread in the decision space, but also performs well with respect to the objective function. The resultant solutions may then serve as a basis solution for the subsequent search while change is occurring. Thus, such an algorithm avoids the clustering of individuals in the same region as well as adapts to changing environments by exploiting diverse promising regions in the solution space. Application of the algorithm to a test problem and a groundwater contaminant source identification problem demonstrates the effectiveness of ADOPT to adaptively identify solutions in dynamic environments.     

A poly-hybrid PSO optimization method with intelligent parameter adjustment

This paper presents results on a new hybrid optimization method which combines the best features of four traditional optimization methods together with an intelligent adjustment algorithm to speed convergence on unconstrained and constrained optimization problems. It is believed that this is the first time that such a broad array of methods has been employed to facilitate synergistic enhancement of convergence. Particle swarm optimization is based on swarm intelligence inspired by the social behavior and movement dynamics of bird flocking, fish schooling, and swarming theory. This method has been applied for structural damage identification, neural network training, and reactive power optimization. It is also believed that this is the first time an intelligent parameter adjustment algorithm has been applied to maximize the effectiveness of individual component algorithms within the hybrid method. A comprehensive sensitivity analysis of the traditional optimization methods within the hybrid group is used to demonstrate how the relationship among the design variables in a given problem can be used to adjust algorithm parameters. The new method is benchmarked using 11 classical test functions and the results show that the new method outperforms eight of the most recently published search methodologies.     

A sequential refinement approach for parameter optimization in continuous dynamic models

The determination of the optimal values for parameters in a continuous dynamic system model is normally a computationally intensive task. Two separate numerical processes are involved; namely, the mechanism for solving the ordinary differential equations that comprise the system model, and the function minimization procedure used to search for the optimal parameter values. Both these processes typically have embedded parameters which control their respective operations. In this paper a general approach is described for adjusting these parameters in a way which allows the two processes to function in a more integrated and hence more efficient way in solving the parameter optimization problem. A specific implementation of the approach is described and the results of an extensive set of numerical experiments are given, These results indicate that the approach can provide a significant advantage in reducing the computational effort.     

Road-traffic monitoring by knowledge-driven static and dynamic image analysis

This article presents a visual application which allows a study and analysis of traffic behavior on major roads (more specifically freeways and highways), using as the main surveillance artefact a video camera mounted on a relatively high place (such as a bridge) with a significant image analysis field. The system described presents something new which is the combination of both traditional traffic monitoring systems, that is, monitoring to get information on different traffic parameters and monitoring to detect accidents automatically. Therefore, we present a system in charge of compiling information on different traffic parameters. It also has a surveillance module for that traffic, which can detect a wide range of the most significant incidents on a freeway or highway.     

A neural network approach to multiple-objective cutting parameter optimization based on fuzzy preference information

This paper presents a neural network approach to multiple-objective cutting parameter optimization for planning turning operations. Productivity, operation cost, and cutting quality are considered as criteria for optimizing machining operations. A feedforward neural network and a dynamic training procedure are proposed for modeling manufacturers' preferences using sampled fuzzy preferential data. Optimum cutting parameters are determined based on neural network representations of manufacturers' fuzzy preference structures.     

Topology optimization of rubber isolators considering static and dynamic behaviours

A topology optimization for the design of rubber vibration isolators is proposed. Many vibration isolators are made of rubbers and they operate under small oscillatory load superimposed on large static deformation. Vibration isolators must have a certain degree of static stiffness in order to endure the static loading due to large gravitational and inertial forces. On the other hand, isolators must have a small dynamic stiffness in order to reduce the force transmission from vibrating systems to base structures. Therefore both the static and dynamic behaviours of rubber should be simultaneously considered in the design process. The static behaviours of rubber under large and slow loads are generally treated with hyperelastic constitutive models. Rubber under fast dynamic loads can be modelled as a viscoelastic material. In this paper, the steady state viscoelastic model, which is suggested by Kim and Youn and correctly predicts the influence of the pre-strain on the relaxation function, is applied for the dynamic analysis. The continuum-based design sensitivity analyses (DSA) of both the static hyperelastic model and dynamic viscoelastic model are developed. The topology optimization formulation is proposed in order to generate the system layouts considering both the static and dynamic performance. The density distribution approach and sequentially linear programming (SLP) are used as the optimization algorithms. Some design examples are presented in order to verify the proposed approach.