The influence of raw material characteristics on the industrial pelletizing process and pellet quality

Industrial pelletizing of sawdust was carried out as a designed experiment in the factors: sawdust moisture content, fractions of fresh pine, stored pine and spruce. The process parameters and response variables were energy consumption, pellet flow rate, pellet bulk density, durability and moisture content. The final data consisted of twelve industrial scale runs. Because of the many response variables, data evaluation was by principal component analysis of a 12 × 9 data matrix. The two principal component model showed a clustering of samples, with a good reproducibility of the center points. It also showed a positive correlation of energy consumption, bulk density and durability all negatively correlated to flow rate and moisture content. The stored pine was more related to high durability and bulk density. The role of the spruce fraction was unclear. The design matrix, augmented with the process parameters was a 12 × 6 matrix. Partial least squares regression showed excellent results for pellet moisture content and bulk density. The model for durability was promising. A 12 × 21 data matrix of fatty- and resin acid concentrations measured by GC–MS showed the differences between fresh and stored pine very clearly. The influence of the spruce fraction was less clear. However, the influence of the fatty- and resin acids on the pelletizing process could not be confirmed, indicating that other differences between fresh and stored pine sawdust have to be investigated. This work shows that it is possible to design the pelletizing process for moderate energy consumption and high pellet quality.     

On solving systems of ordinary differential equations on MIMD-computers

The error of solution of Cauchy problems for systems of ordinary differential equations is estimated in the case where the input data are approximate. It is shown how to prepare a program for computing the right-hand sides of the system automatically and simultaneously. Diagrams are presented to illustrate the efficiency of parallelization. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 2, pp. 175–182, March–April 2007.     

Identification of the minimum effective dose for right-censored survival data

In this paper, we consider identifying the minimum effective dose (MED) in a dose-response study when survival data are subject to random right-censorship, where the MED is defined to be the smallest dose level under study that has survival advantage over the zero-dose control. To this end, we suggest single-step-down testing procedures based on three different types of weighted logrank statistics, respectively. The comparative results of a Monte Carlo error rate and power/bias study for a variety of survival and censoring distributions are then presented and discussed. The application of the testing procedures for identifying the MED is finally illustrated by using a numerical example of prostate cancer data.     

基于随机分析和概率预测的固定式海洋平台结构风险评估研究

对极端海况进行了环境荷载的联合概率分析。引入了整体不确定性和敏感性分析理论,利用非正态过程、具有不同相关性的多变量随机模拟技术,求解结构的失效概率。提出结构失效分析的新方法,并利用DNV提供的历史数据,对结构失效的后果做了适当分析。以埕北12-C井组平台为实例,对平台甲板高程(Air Gap)失效分析进行了实例计算。结果表明,使用多维联合极值分布理论及相应的求解方法,是离岸工程结构物风险评估的重要手段。     

小波包变换压缩与地震数据处理

探讨了地震数据经小波包变换压缩后对后续处理过程的影响,以及经过某些处理后的数据会对小波包变换压缩产生什么影响等问题。将小波包变换压缩地震数据的结果与快速傅里叶变换压缩地震数据的结果进行了对比,其结果表明:应用小波包变换压缩地震数据,有效信号的损失非常小,压缩比大于快速傅里叶变换的压缩比。在大批量地震数据处理中可以使用小波包变换压缩地震数据。     

松辽盆地南部岩性圈闭识别技术及效果

寻找隐蔽油气藏一直是油气勘探中的难题,我国东部吉林油田的中浅层已进入高成熟勘探阶段,油气勘探已由早期的构造圈闭勘探转变为现今的以隐蔽圈闭勘探为主的阶段。本文针对松辽盆地南部的西部前缘带三维连片工区及伊通地堑岔路河断陷,综合应用构造精细解释、声波曲线重构波阻抗反演、层序地层学、地震相及沉积(微)相分析、模型正演、地震属性分析、频谱分解及三维可视化等技术手段,所提供的多口探井获得工业油气流或有良好的油气显示,取得了较好的解释效果。     

Battery-Aware Routing for Streaming Data Transmissions in Wireless Sensor Networks

Recent technological advances have made it possible to support long lifetime and large volume streaming data transmissions in sensor networks. A major challenge is to maximize the lifetime of battery-powered sensors to support such transmissions. Battery, as the power provider of the sensors, therefore emerges as the key factor for achieving high performance in such applications. Recent study in battery technology reveals that the behavior of battery discharging is more complex than we used to think. Battery powered sensors might waste a huge amount of energy if we do not carefully schedule and budget their discharging. In this paper we study the effect of battery behavior on routing for streaming data transmissions in wireless sensor networks. We first give an on-line computable energy model to mathematically model battery discharge behavior. We show that the model can capture and describe battery behavior accurately at low computational complexity and thus is suitable for on-line battery capacity computation. Based on this battery model we then present a battery-aware routing (BAR) protocol to schedule the routing in wireless sensor networks. The routing protocol is sensitive to the battery status of routing nodes and avoids energy loss. We use the battery data from actual sensors to evaluate the performance of our protocol. The results show that the battery-aware protocol proposed in this paper performs well and can save a significant amount of energy compared to existing routing protocols for streaming data transmissions. Network lifetime is also prolonged with maximum data throughput. As far as we know, this is the first work considering battery-awareness with an accurate analytical on-line computable battery model in sensor network routing. We believe the battery model can be used to explore other energy efficient schemes for wireless networks as well.     

VC++实现的串口通讯数据处理

说明了在异步串行通信(RS-232)的工作方式下,对所接收的数据进行接收、处理,丢弃乱码,并进行保存的可靠方法,并给出了相关的VC 语言程序.     

Logistic Regression Analysis of Customer Satisfaction Data

Variation exists in all processes. Significant work has been done to identify and remove sources of variation in manufacturing processes resulting in large returns for companies. However, business process optimization is an area that has a large potential return for a company. Business processes can be difficult to optimize due to the nature of the output variables associated with them. Business processes tend to have output variables that are binary, nominal or ordinal. Examples of these types of output include whether a particular event occurred, a customer's color preference for a new product and survey questions that assess the extent of the survey respondent's agreement with a particular statement. Output variables that are binary, nominal or ordinal cannot be modeled using ordinary least‐squares regression. Logistic regression is a method used to model data where the output is binary, nominal or ordinal. This article provides a review of logistic regression and demonstrates its use in modeling data from a business process involving customer feedback. Copyright © 2006 John Wiley & Sons, Ltd.     

射频识别模块SMC51489在门禁系统中的应用

射频识别系统一般由三部分组成:电子标签(射频卡)、读取器和天线.正确快速的识别电子标签的标签号码,是门禁系统发挥其功能的先决条件.以无源低频射频卡识别模块SMC51489为例,介绍了对射频卡信息读取的原理和方法,并给出了相应的软件编程.实验证明该模块不仅具有较大的读卡距离,而且工作可靠.