Cognitive Trauma Therapy for Battered Women With PTSD (CTT-BW).

This article describes a second treatment-outcome study of cognitive trauma therapy for battered women with posttraumatic stress disorder (PTSD; CTT-BW). CTT-BW includes trauma history exploration: PTSD education; stress management; exposure to abuse and abuser reminders; self-monitoring of negative self-talk; cognitive therapy for guilt; and modules on self-advocacy, assertiveness, and how to identify perpetrators. One hundred twenty-five ethnically diverse women were randomly assigned to immediate or delayed CTT-BW. PTSD remitted in 87% of women who completed CTT-BW, with large reductions in depression and guilt and substantial increases in self-esteem. White and ethnic minority women benefited equally from CTT-BW. Similar treatment outcomes were obtained by male and female therapists and by therapists with different levels of education and training. Gains were maintained at 3- and 6-month follow-ups. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

光纤光栅传感技术在大结构监测中的应用进展

在众多的传感器种类中,利用多个光纤光栅传感器可构成多路和分布式传感器等各种形式的光纤传感网络等优点,使它正在成为传感器领域中新的研究方向。光纤光栅传感器所具有的独特的技术优势使其非常适合用于桥梁、堤坝、建筑物、航空航天、航海、海洋石油平台及油田等大结构工程的监测。论述了该技术的特点和近年来在国外大结构中的应用。     

设施栽培条件下土壤养分及有机质组成变化趋势的研究

以黄泛平原壤质潮土不同年限的设施栽培土壤为研究对象,探讨了设施栽培条件对土壤养分及土壤有机质组成的影响。结果表明,设施栽培土壤碱解氮、速效磷、速效钾、盐分含量均明显高于大田,其含量主要取决于施用化学肥料的种类和数量,随栽培年限的增加而增加;土壤有机质、腐殖酸、胡敏酸含量随种植年限有升高趋势;HA/FA与栽培年限显著相关,表示设施栽培条件下,土壤熟化程度随有机肥料的逐年施用而有所提高,但HA/FA变化幅度不大,这可能与设施栽培形成的高温高湿条件及施用有机肥料中难分解物质的含量有关。     

A Distributed Approach for Monitoring Multicast Service Availability

With the deployment of native multicast in commercial networks, multicast is getting closer to becoming a ubiquitous service in the Internet. The success of this deployment largely depends on the availability of good management tools and systems. One of the most important management tasks for multicast is to verify the availability of the service to its users. This task is usually referred to as reachability monitoring. Reachability monitoring requires a number of monitoring stations to work together to collect this information in a distributed manner in the interdomain scale. In this paper we present a general architecture for multicast reachability monitoring systems and focus on three critical functions: agent configuration, monitoring, and feedback collection. For each component, we provide a number of alternative approaches to implement the required functionality and discuss their advantages and disadvantages. Then, we focus on the feedback collection component. To a large extent, it determines the complexity and the overhead of a monitoring system. Using simulations, we compare a number of alternative approaches for feedback collection and make suggestions on when to use each. We believe our work provides insight into the issues and considerations in designing and developing multicast reachability monitoring systems.     

Intelligent condition monitoring using fuzzy inductive learning

Extensive research has been performed for developing knowledge based intelligent monitoring systems for improving the reliability of manufacturing processes. Due to the high expense of obtaining knowledge from human experts, it is expected to develop new techniques to obtain the knowledge automatically from the collected data using data mining techniques. Inductive learning has become one of the widely used data mining methods for generating decision rules from data. In order to deal with the noise or uncertainties existing in the data collected in industrial processes and systems, this paper presents a new method using fuzzy logic techniques to improve the performance of the classical inductive learning approach. The proposed approach, in contrast to classical inductive learning method using hard cut point to discretize the continuous-valued attributes, uses soft discretization to enable the systems have less sensitivity to the uncertainties and noise. The effectiveness of the proposed approach has been illustrated in an application of monitoring the machining conditions in uncertain environment. Experimental results show that this new fuzzy inductive learning method gives improved accuracy compared with using classical inductive learning techniques.     

Distributed machining control and monitoring using smart sensors/actuators

The study of smart sensors and actuators led, during the past few years, to the development of facilities which improve traditional sensors and actuators in a necessary way to automate production systems. In another context, many studies have been carried out aimed at defining a decisional structure for production activity control and the increasing need of reactivity leads to the autonomization of decisional levels close to the operational system. We study in this paper the natural convergence between these two approaches and we propose an integration architecture, dealing with machine tool and machining control, that enables the exploitation of distributed smart sensors and actuators in the decisional system.     

Java-MaC: A Run-Time Assurance Approach for Java Programs

We describe Java-MaC, a prototype implementation of the Monitoring and Checking (MaC) architecture for Java programs. The MaC architecture provides assurance that the target program is running correctly with respect to a formal requirements specification by monitoring and checking the execution of the target program at run-time. MaC bridges the gap between formal verification, which ensures the correctness of a design rather than an implementation, and testing, which does not provide formal guarantees about the correctness of the system.Use of formal requirement specifications in run-time monitoring and checking is the salient aspect of the MaC architecture. MaC is a lightweight formal method solution which works as a viable complement to the current heavyweight formal methods. In addition, analysis processes of the architecture including instrumentation of the target program, monitoring, and checking are performed fully automatically without human direction, which increases the accuracy of the analysis. Another important feature of the architecture is the clear separation between monitoring implementation-dependent low-level behaviors and checking high-level behaviors, which allows the reuse of a high-level requirement specification even when the target program implementation changes. Furthermore, this separation makes the architecture modular and allows the flexibility of incorporating third party tools into the architecture. The paper presents an overview of the MaC architecture and a prototype implementation Java-MaC.     

Connectivity oriented fast Hough transform for tool wear monitoring

Tool wear monitoring can be achieved by analyzing the texture of machined surfaces. In this paper, we present the new connectivity oriented fast Hough transform, which easily detects all line segments in binary edge images of textures of machined surfaces. The features extracted from line segments are found to be highly correlated to the level of tool wear. A multilayer perceptron neural network is applied to estimate the flank wear in various machining processes. Our experiments show that this Hough transform based approach is effective in analyzing the quality of machined surfaces and could be used to monitor tool wear. A performance analysis of our Hough transform is also provided.     

基于KELM和多传感器信息融合的风电齿轮箱故障诊断

为提高风电齿轮箱的运行效率,降低风电场的运行维护成本,结合时域统计特征分析和多传感器信息融合技术,提出了一种基于灰狼优化核极限学习机(GWO-KELM)的风电齿轮箱状态监测新方法。首先,计算原始振动信号不同的时域统计特征参数,并采用并行叠加的方式对特征级和数据级进行信息融合以得到融合数据集。其次,利用融合数据集,建立了基于GWO-KELM的故障分类识别模型。最后,运用所提方法对QPZZ-Ⅱ旋转机械振动试验台齿轮箱实测数据进行状态监测,实例结果表明了该方法的有效性和可行性,与其他同类方法相比,所提方法具有最佳分类性能。     

基于虚拟技术的发电机组故障诊断与在线监测

介绍了汽轮发电机组的具体故障形式以及转子的不同故障对应的转子轴心轨迹，分别计算了汽轮发电机组在3种不同状态下的关联雏数，开发了基于Lab VIEW的汽轮发电机组故障诊断与在线监测系统，综合利用频谱分析技术、小波分析技术以及分形理论，同时监测发电机转子轴心轨迹和轴承温度可提高发电机组故障诊断的命中率。