Dynamics analysis of a new simple chaotic attractor

In this paper, a novel simple chaotic system is discussed. Some basic dynamical properties, such as Lyapunov exponents, Poincaré mapping, fractal dimension, bifurcation diagram, continuous spectrum and chaotic dynamical behaviors of the novel chaotic system are studied, either numerically or analytically. The obtained results show clearly that the system discussed in this Letter is a novel chaotic system and deserves a further detailed investigation.     

A simple photolithography method for microfluidic device fabrication using sunlight as UV source

A straightforward method for microfluidic devices fabrication using sunlight as the ultraviolet (UV) source is established in this work. This method is based on photolithography, but obviates the need for specialized UV exposure facility. Substrates coated with photoresist were placed directly under sun in a perpendicular direction to the sunlight for exposure. Exposure conditions were optimized for patterning features with different kinds of photoresist, photoresist of different thicknesses and dimensions. Exposure time can be adjusted to obtain designed features on a mask with good lateral structure according to the energy measured by UV meter (with a constant intensity of UV in sunlight). Masters produced under optimum exposure conditions were used for the fabrication of several microfluidic devices with different materials, structures, or functions. Resultant devices were shown eminently suitable for microfluidic applications such as electrophoretic separation, multiple gradient generator, and pneumatic valve-based cell culture. This photolithographic method is simple, low cost, easy to operate, and environmental friendly. Especially, the masters can be obtained in parallel simultaneously, which is suitable for chip fabrication for mass production. It is also more attractive for the laboratories, in which the support for photolithographic facility is not available.     

Manipulation and patterning of carbon nanotubes utilizing optically induced dielectrophoretic forces

This study reports an optically driven platform upon which the manipulation and patterning of carbon nanotubes (CNTs) can be accomplished. A photoconductive layer made of amorphous silicon generates a nonuniform electric field within the developed platform at specific optically illuminated sites, which are usually referred to as “virtual electrodes,” that induces dielectrophoretic forces for manipulating the CNTs. The software-controlled light patterns enable a variety of flexible manipulation modes since it is now possible to dynamically reconfigure the optically projected electrode patterns. This approach allows for real-time manipulation and miscellaneous patterning of CNTs. The sorting and separation of bundled and dispersed CNTs is also demonstrated. This developed platform may be promising for rapid fabrication of CNT-based nanosensors together with nanoelectronics, purification as well as classification of synthesized CNTs and other applications requiring nanoscale manipulation.     

Improved image interpreting and modeling technique for automated structural optimization system

The automated structural optimization system (ASOS) proposed in the previous work incorporates the image-preprocessing techniques, the image-interpreting technique, and the automated shape-optimization modeling techniques to successfully obtain an autonomously integrated topology and shape optimization. However, the characteristic value-based image-interpreting technique used in ASOS is unable to accurately interpret complicated hole shapes, necessitating the use of the hole shape-adjusting strategy in addition to the hole-expanding strategy and the interference analysis in the automated shape-optimization modeling techniques to obtain a viable initial design and side constraints of design variables. In order to solve the above-mentioned problem in ASOS, this paper proposes the improved automated structural optimization system (IASOS) and uses the polygonal image-interpreting technique to replace the characteristic value-based image-interpreting technique used in ASOS. This alteration significantly increases the accuracy of image interpretation. Moreover, it can simplify the process of automated shape-optimization modeling techniques, reduce the design duration, and produce better results.     

EXAM: a comprehensive environment for the analysis of access control policies

Policy integration and inter-operation is often a crucial requirement when parties with different access control policies need to participate in collaborative applications and coalitions. Such requirement is even more difficult to address for dynamic large-scale collaborations, in which the number of access control policies to analyze and compare can be quite large. An important step in policy integration and inter-operation is to analyze the similarity of policies. Policy similarity can sometimes also be a pre-condition for establishing a collaboration, in that a party may enter a collaboration with another party only if the policies enforced by the other party match or are very close to its own policies. Existing approaches to the problem of analyzing and comparing access control policies are very limited, in that they only deal with some special cases. By recognizing that a suitable approach to the policy analysis and comparison requires combining different approaches, we propose in this paper a comprehensive environment—EXAM. The environment supports various types of analysis query, which we categorize in the paper. A key component of such environment, on which we focus in the paper, is the policy analyzer able to perform several types of analysis. Specifically, our policy analyzer combines the advantages of existing MTBDD-based and SAT-solver-based techniques. Our experimental results, also reported in the paper, demonstrate the efficiency of our analyzer.     

大型压缩机组设备群的故障预测与健康管理

介绍了压缩机组设备群—复杂系统的故障预示和健康状态管理技术和系统的研究。融合早期故障预示、综合故障诊断和远程专家诊断技术,研究设备群的健康状态管理技术,并研发了压缩机组设备群故障诊断和健康状态管理系统,实现主辅机故障诊断和健康状态管理一体化、监测控制信息利用共享化、生产企业和设备制造企业知识资源利用最大化,为石化生产企业和设备制造企业提供一套全生命周期的压缩机组设备群故障诊断和健康管理的系统解决方案。     

Gene selection and sample classification on microarray data based on adaptive genetic algorithm/k-nearest neighbor method

Recently, microarray technology has widely used on the study of gene expression in cancer diagnosis. The main distinguishing feature of microarray technology is that can measure thousands of genes at the same time. In the past, researchers always used parametric statistical methods to find the significant genes. However, microarray data often cannot obey some of the assumptions of parametric statistical methods, or type I error may be over expanded. Therefore, our aim is to establish a gene selection method without assumption restriction to reduce the dimension of the data set. In our study, adaptive genetic algorithm/k-nearest neighbor (AGA/KNN) was used to evolve gene subsets. We find that AGA/KNN can reduce the dimension of the data set, and all test samples can be classified correctly. In addition, the accuracy of AGA/KNN is higher than that of GA/KNN, and it only takes half the CPU time of GA/KNN. After using the proposed method, biologists can identify the relevant genes efficiently from the sub-gene set and classify the test samples correctly.     

Analysis by data mining in the emergency medicine triage database at a Taiwanese regional hospital

“Emergency medicine” is the front line of medical service a hospital provides; also it is the department people seek medical care from immediately after an emergency happens. The statistics by the Department of Health, Executive Yuan, indicate that over years, the number of people at the emergency department has been increasing. The US has introduced and practiced the triage system in the emergency medicine in 1960, whereby to aid the emergency department in allocating the patients, to give them appropriate medical care by the fast decision of the nurses and doctors in case of the patients’ seriousness through their judgment.This study takes on the knowledge contained in the massive data of unknown characteristics in the triage database at a Taiwanese regional hospital, using the cluster analysis and the rough set theory as tools for data mining to extract, with the analysis software ROSE2 (Rough Sets Data Explorer) and through rule induction technique, the imprecise, uncertain and vague information of rules from the massive database, and builds the model that is capable of simplifying massive data while maintaining the accuracy in classifying rules. After analyzing and evaluating the knowledge obtained from relevant mining in the hospitals past medical data for the consumption of emergency medical resources, this thesis proposes suggestions as reference for the hospitals in subsequent elevation of medical quality and decrease in operative costs.     

Toward intelligent data warehouse mining: An ontology-integrated approach for multi-dimensional association mining

A data warehouse is an important decision support system with cleaned and integrated data for knowledge discovery and data mining systems. In reality, the data warehouse mining system has provided many applicable solutions in industries, yet there are still many problems causing users extra problems in discovering knowledge or even failing to obtain the real and useful knowledge they need. To improve the overall data warehouse mining process, we present an intelligent data warehouse mining approach incorporated with schema ontology, schema constraint ontology, domain ontology and user preference ontology. The structures of these ontologies are illustrated and how they benefit the mining process is also demonstrated by examples utilizing rule mining. Finally, we present a prototype multidimensional association mining system, which with intelligent assistance through the support of the ontologies, can help users build useful data mining models, prevent ineffective pattern generation, discover concept extended rules, and provide an active knowledge re-discovering mechanism.     

An integrated safety prognosis model for complex system based on dynamic Bayesian network and ant colony algorithm

In complex industrial system, most of single faults have multiple propagation paths, so any local slight deviation is able to propagate, spread, accumulate and increase through system fault causal chains. It will finally result in unplanned outages and even catastrophic accidents, which lead to huge economic losses, environmental contamination, or human injuries. In order to ensure system intrinsic safety and increase operational performance and reliability in a long period, this study proposes an integrated safety prognosis model (ISPM) considering the randomness, complexity and uncertainty of fault propagation.ISPM is developed based on dynamic Bayesian networks to model the propagation of faults in a complex system, integrating the priori knowledge of the interactions and dependencies among subsystems, components, and the environment of the system, as well as the relationships between fault causes and effects. So the current safety state and potential risk of system can be assessed by locating potential hazard origins and deducing corresponding possible consequences. Furthermore, ISPM is also developed to predict the future degradation trend in terms of future reliability or performance of system, and provide proper proactive maintenance plans. Ant colony algorithm is introduced in ISPM by comprehensively considering two factors as probability and severity of faults, to perform the quantitative risk estimation of the underlining system. The feasibility and benefits of ISPM are investigated with a field case study of gas turbine compressor system. According to the outputs given by ISPM in the application, proactive maintenance, safety-related actions and contingency plans are further discussed and then made to keep the system in a high reliability and safety level in the long term.