Online mining abnormal period patterns from multiple medical sensor data streams

With the advanced technology of medical devices and sensors, an abundance of medical data streams are available. However, data analysis techniques are very limited, especially for processing massive multiple physiological streams that may only be understood by medical experts. The state-of-the-art techniques only allow multiple medical devices to independently monitor different physiological parameters for the patient’s status, thus they signal too many false alarms, creating unnecessary noise, especially in the Intensive Care Unit (ICU). An effective solution which has been recently studied is to integrate information from multiple physiologic parameters to reduce alarms. But it is a challenge to detect abnormalities from high frequently changed physiological streams data, since abnormalities occur gradually due to the complex situation of patients. An analysis of ICU physiological data streams shows that many vital physiological parameters are changed periodically (such as heart rate, arterial pressure, and respiratory impedance) and thus abnormalities are generally abnormal period patterns. In this paper, we develop a Mining Abnormal Period Patterns from Multiple Physiological Streams (MAPPMPS) method to detect and rank abnormalities in medical sensor streams. The efficiency and effectiveness of the MAPPMPS method is demonstrated by a real-world massive database of multiple physiological streams sampled in ICU, comprising 250 patients’ streams (each stream involving over 1.3 million data points) with a total size of 28 GB data.     

A hybrid clustering algorithm based on PSO with dynamic crossover

In order to overcome the premature convergence in particle swarm optimization (PSO), we introduce dynamical crossover, a crossover operator with variable lengths and positions, to PSO, which is briefly denoted as CPSO. To get rid of the drawbacks of only finding the convex clusters and being sensitive to the initial points in $k$ -means algorithm, a hybrid clustering algorithm based on CPSO is proposed. The difference between the work and the existing ones lies in that CPSO is firstly introduced into $k$ -means. Experimental results performing on several data sets illustrate that the proposed clustering algorithm can get completely rid of the shortcomings of $k$ -means algorithms, and acquire correct clustering results. The application in image segmentation illustrates that the proposed algorithm gains good performance.     

Echo state networks based predictive model of vinyl chloride monomer convention velocity optimized by artificial fish swarm algorithm

Conversion rate in the Polyvinylchloride (PVC) polymerizing process has a certain influence on the molecular weight of PVC, porosity, absorption rate of plasticizer, vinyl chloride monomer (VCM) residue and thermal stability. Therefore, a predictive model based on echo state networks (ESN) method optimized by the artificial fish swarm algorithm (AFSA) is proposed to predict the conversion velocity. Firstly, the hot balancing mechanisms of polymerizer and the influenced factors of convention rate of VCM are analyzed in details. Then the auxiliary variables of the predictive model kernel are selected by using the kernel principal component analysis method for reducing the model dimensionality. Thirdly, the structure parameters of the ESN are optimized by the AFSA to realize the nonlinear mapping between input and output variables of the discussed soft-sensor model. The artificial fish swarm behaviors, such as foraging, swarming, chasing, random, are introduced in details. Finally, simulation results show that the proposed model can significantly enhance the predictive accuracy and robustness of the technical index and satisfy the real-time control requirements of PVC polymerizing production process.     

锦屏一级水电站左岸底层灌浆平洞深孔帷幕灌浆施工

锦屏一级水电站作为世界第一高的拱坝,在超高的水头和复杂的地质条件下,在底层灌浆洞中进行了超过170 m的深孔帷幕灌浆。施工中克服了各种不利条件,解决了多种非常规的施工技术难题,其经验可为其他类似工程提供参考。     

壳聚糖固定化脲酶生物传感器选择性测定铜离子

基于重金属对脲酶的抑制作用,研制了用于测定铜离子的生物传感器。该生物传感器的制备以壳聚糖为载体,将脲酶固定于pH电极表面。由于壳聚糖对Cu2+的富集,该生物传感器展现出高灵敏度。在样品溶液中加入5 mmol/L NaI,可以消除Hg2+和Ag+的干扰,从而实现Cu2+的选择性检测。在0.005～0.5μg/mL的浓度范围内,脲酶活性的抑制率与Cu2+浓度的对数呈良好的线性响应关系,其检出限为0.002μg/mL。将使用后的生物传感器浸泡于0.5 mmol/L的EDTA溶液再生5 min,被Cu2+抑制的脲酶的活性可以得到恢复。     

软件测试工具分析及其应用探讨

结合实践探讨了在现有生产软件的中小型企业中,如何将软件测试与软件开发相结合,使软件测试管理的质量得以提升。介绍了一些目前常用的软件测试工具及测试管理工具,对软件测试管理中应注意到的80:20问题进行阐述分析,提出对测试管理中可能存在的风险可采取一些方法来应对。实践证明,使用全程软件测试管理对解决测试管理工作中存在的问题有很大帮助。     

海洋经济运行监测系统的设计与实现

根据省级海洋经济运行监测能力建设的需要和对系统安全等级的要求,以JAVA为开发语言,以Oracle 11g为后台数据库,开发设计了海洋经济运行监测系统。介绍了该系统的设计思想、实现技术和其安全保障机制。该系统的实现,为沿海省份提供了一种快捷准确的海洋经济数据采集方案,提高了海洋经济运行监测能力,以期为地方海洋经济的发展提供更为科学、准确的数据支撑。     

An alternate quantum adiabatic evolution for the Deutsch–Jozsa problem

Deutsch–Jozsa algorithm has been implemented via quantum adiabatic evolutions by Das et al. (Phys Rev A 65:062310, 2002) and Wei et al. (Phys Lett A 354:271, 2006). In the latter literature, the authors have shown a modified version of the adiabatic evolution which can improve the performance of the algorithm of S. Das et al’s to constant time. In this paper, we also improve the algorithm of S. Das et al’s in a constant time but by using a different construction of adiabatic evolution, i.e., adding ancillary qubits. The algorithm in this paper provides an alternative option to potential users.     

Optimality of partial adiabatic search and its circuit model

In this paper, we first uncover a fact that a partial adiabatic quantum search with \(O(\sqrt{N/M})\) time complexity is in fact optimal, in which \(N\) is the total number of elements in an unstructured database, and \(M\) ( \(M\ge 1\) ) of them are the marked ones(one) \((N\gg M)\) . We then discuss how to implement a partial adiabatic search algorithm on the quantum circuit model. From the implementing procedure on the circuit model, we can find out that the approximating steps needed are always in the same order of the time complexity of the adiabatic algorithm.     

基于组件的大数据分析服务平台

随着数据规模的快速增长,单机的数据分析工具已经无法满足需求。针对大数据的分析问题,设计并实现了一种基于组件的大数据分析服务平台Haflow。Haflow自定义了业务流程模型和可扩展的组件接口,组件接口支持各种异构工具的集成。系统接收用户定义的业务流程,将其翻译成执行流程实例,提交到Hadoop分布式集群上执行。Haflow是一个可扩展的、分布式的、支持异构分析工具的、面向服务的大数据分析服务平台。提出该平台有两重意义:一方面平台将与数据分析业务无关的工作封装起来,支持各种异构组件,以加快分析应用的开发速度;另一方面,平台后端使用Hadoop分布式系统来实现多任务的并发,从而提高应用的平均执行速度。