基于支持向量机方法对非平稳时间序列的预测

目前有关非平稳复杂系统及其在预测中的应用研究是一个较少被人理解并有重大科学意义的前瞻性研究课题.在大气运动中,气候正是一个典型的非平稳系统,但是现有的气候预测理论,包括统计预测理论和非线性预测理论,几乎都无一例外地建立在平稳性假定的基础之上,这有悖于气候过程的基本性质,它有可能是导致气候预测水平低下的重要的理论原因.因此以分析如何降低时间序列非平稳程度作为切入点来研究短期气候预测问题有着重要的理论意义.利用基于“升维”思想的支持向量机方法对时变控制参数条件下Lorenz系统产生的非平稳时间序列以及来自实际     

Phase measurement accuracy of feedback interferometers

Feedback interferometers are described with specific reference to potential applications in micro-machines. A theoretical analysis is developed to determine the linearity, stability, and noise performance of this type of interferometer. The theoretical analysis was tested using a prototype high-precision feedback interferometer which showed that, at a feedback loop gain which enabled the system to track 6 fringes, the linearity of the interferometer was better than λ/100 and single phase measurements could be made with an accuracy of λ/80.     

An algorithmic approach for a special class of Markov chains

In this paper, we develop an algorithmic method for the evaluation of the steady state probability vector of a special class of finite state Markov chains. For the class of Markov chains considered here, it is assumed that the matrix associated with the set of linear equations for the steady state probabilities possess a special structure, such that it can be rearranged and decomposed as a sum of two matrices, one lower triangular with nonzero diagonal elements, and the other an upper triangular matrix with only very few nonzero columns. Almost all Markov chain models of queueing systems with finite source and/or finite capacity and first-come-first-served or head of the line nonpreemptive priority service discipline belongs to this special class.     

Single Machine Scheduling of Unit-time Jobs with Controllable Release Dates

The paper presents a bicriterion approach to solve the single-machine scheduling problem in which the job release dates can be compressed while incurring additional costs. The two criteria are the makespan and the compression cost. For the case of equal job processing times, an O(n⁴) algorithm is developed to construct integer Pareto optimal points. We discuss how the algorithm developed can be modified to construct an -approximation of noninteger Pareto optimal points. The complexity status of the problem with total weighted completion time criterion is also established.     

Production systems with interruptions, arbitrary topology and finite buffers

We consider a production system with finite buffers and arbitrary topology where service time is subject to interruptions in one of three ways, viz. machine breakdown, machine vacations or Npolicy. We develop a unified approximation (analytical) methodology to calculate the throughput of the system using queueing networks together with decomposition, isolation and expansion techniques. The methodology is rigorously tested covering a large experimental region. Orthogonal arrays are used to design the experiments in order to keep the number of experiments manageable. The results obtained using the approximation methodology are compared to the simulation results. The ttests carried out to investigate the differences between the two results show that they are statistically insignificant. Finally, we test the methodology by applying it to several arbitrary topology networks. The results show that the performance of the approximation methodology is consistent, robust and produces excellent results in a variety of experimental conditions.     

双热源空调-热水器一体机冬季制热的实验研究

冬季室外温度低时,空气源热泵系统的蒸发器会结霜,使系统COP降低。所设计的空调-热水器一体机可以制冷、制热、一年四季提供生活用热水。冬季室外温度低时,用太阳能加热后的水作为热泵的低温热源,可以提高热泵的效率。分别用空气源蒸发器和水源蒸发器独立工作使系统给房间制热,实验结果发现水源蒸发器工作时系统的COP平均值为3.56,空气源蒸发器工作时的COP平均值为2.51。     

基于选择性支持向量机集成的混沌时间序列预测

提出了一种基于聚类的选择性支持向量机集成预测模型.为提高支持向量机集成的泛化能力，采用自组织映射和K均值聚类算法结合的聚类组合算法，从每簇中选择出精度最高的子支持向量机进行集成，可以保证子支持向量机有较高精度并提高了子支持向量机之间的差异度.该方法能以较小的代价显著提高支持向量机集成的泛化能力.采用该方法对Mackey-Glass混沌时间序列和Lorenz系统生成的混沌时间序列进行预测实验，结果表明可以对混沌时间序列进行准确预测，验证了该方法的有效性. 关键词： 支持向量机 集成 混沌时间序列 聚类     

The Chemistry of Cyborgs—Interfacing Technical Devices with Organisms

The term “cyborg” refers to a cybernetic organism, which characterizes the chimera of a living organism and a machine. Owing to the widespread application of intracorporeal medical devices, cyborgs are no longer exclusively a subject of science fiction novels, but technically they already exist in our society. In this review, we briefly summarize the development of modern prosthetics and the evolution of brain–machine interfaces, and discuss the latest technical developments of implantable devices, in particular, biocompatible integrated electronics and microfluidics used for communication and control of living organisms. Recent examples of animal cyborgs and their relevance to fundamental and applied biomedical research and bioethics in this novel and exciting field at the crossroads of chemistry, biomedicine, and the engineering sciences are presented.     

Choiceless polynomial time

Turing machines define polynomial time (PTime) on strings but cannot deal with structures like graphs directly, and there is no known, easily computable string encoding of isomorphism classes of structures. Is there a computation model whose machines do not distinguish between isomorphic structures and compute exactly PTime properties? This question can be recast as follows: Does there exist a logic that captures polynomial time (without presuming the presence of a linear order)? Earlier, one of us conjectured a negative answer. The problem motivated a quest for stronger and stronger PTime logics. All these logics avoid arbitrary choice. Here we attempt to capture the choiceless fragment of PTime. Our computation model is a version of abstract state machines (formerly called evolving algebras). The idea is to replace arbitrary choice with parallel execution. The resulting logic expresses all properties expressible in any other PTime logic in the literature. A more difficult theorem shows that the logic does not capture all of PTime.     

Misalignment Fault Prediction of Wind Turbines Based on Improved Artificial Fish Swarm Algorithm

A misalignment fault is a kind of potential fault in double-fed wind turbines. The reasonable and effective fault prediction models are used to predict its development trend before serious faults occur, which can take measures to repair in advance and reduce human and material losses. In this paper, the Least Squares Support Vector Machine optimized by the Improved Artificial Fish Swarm Algorithm is used to predict the misalignment index of the experiment platform. The mixed features of time domain, frequency domain, and time-frequency domain indexes of vibration or stator current signals are the inputs of the Least Squares Support Vector Machine. The kurtosis of the same signals is the output of the model, and the 3σ principle of the normal distribution is adopted to set the warning line of misalignment fault. Compared with other optimization algorithms, the experimental results show that the proposed prediction model can predict the development trend of the misalignment index with the least prediction error.