基于混沌免疫粒子群优化和广义回归神经网络的回采工作面瓦斯涌出量预测模型

为提高回采工作面绝对瓦斯涌出量预测的精度和效率,提出了将混沌免疫粒子群优化(CIPSO)算法与广义回归神经网络(GRNN)相耦合的绝对瓦斯涌出量预测模型。该方法采用CIPSO对GRNN的光滑因子进行动态优化调整,减少了人为因素对GRNN网络输出结果的影响,并采用优化后的网络建立瓦斯涌出量预测模型。通过对某煤矿瓦斯涌出量数据的仿真实验结果表明:基于CIPSO-GRNN的回采工作面绝对瓦斯涌出量模型比BP神经网络、Elman网络预测模型具有更好的预测精度和收敛速度,证明了该方法的有效性和可行性。     

灰色Elman神经网络的矿井瓦斯涌出量预测

为了准确预测矿井瓦斯涌出量,将灰色理论与Elman神经网络模型结合,建立矿井瓦斯涌出量预测模型。灰色系统能较好地预测变化的趋势,而Elman神经网络具有动态特性好、逼近速度快、精度高等特点。对于煤矿生产中瓦斯涌出量的预测,两者结合能够发挥各自的优势,以某煤矿矿井为例,影响瓦斯涌出量的因素为预测因子建立灰色理论与Elman神经网络融合的预测模型。结果表明,灰色Elman神经网络模型优于传统灰色预测模型,提高了预测精度,达到了很好的预测效果。     

遗传优化的GNNM在瓦斯涌出量预测中的应用

研究矿井瓦斯涌出量准确预测一直是煤矿安全生产中重点关注的问题。煤层瓦斯爆炸因受开发环境、矿层深度、天气等因素的影响,造成与瓦斯涌出量增大而引起的。针对传统预测模型在矿井瓦斯涌出量预测中存在建模困难、收敛速度慢、要求历史数据量大的问题,提出了一种遗传优化的灰色神经网络预测模型。模型利用灰色系统对数据量要求低的特点,将灰色系统理论与神经网络有机结合起来,建立灰色神经网络模型。并采用遗传算法对所建立模型的权值和阈值进行优化。采用模型对矿井瓦斯涌出量进行预测,实验表明,遗传优化的灰色神经网络模型,可以简化系统建模,并能提高瓦斯涌出量预测精度,有一定的实用价值。     

最小二乘支持向量机与Kalman滤波耦合的瓦斯涌出量动态预测模型

针对瓦斯涌出量的多影响因素预测问题,提出一种最小二乘支持向量机(LS-SVM)回归算法与卡尔曼滤波耦合的动态预测方法.该方法依据预测残差方差比检验策略确定自适应的动态训练样本集以取代固定的训练样本集.LS-SVM辨识网络对瓦斯涌出量的相关因素进行非线性映射并提取出最佳维数的状态向量以建立基于卡尔曼滤波最优估计的瓦斯涌出量预测模型.利用矿井监测到的各项历史数据进行实验.结果表明,该模型的预测平均相对误差为2.17%,平均相对变动值ARV为0.008873,相比单一的神经网络或支持向量机预测模型,具有更高的预测精度与更强的泛化能力.     

煤矿瓦斯涌出量动态预测的PCA-MFOA-GRNN模型及应用

针对煤矿瓦斯涌出受许多因素的影响,为了克服瓦斯涌出中存在的复杂的非线性关系,从而实现稳定、可靠、精确的对煤矿综采工作面瓦斯涌出量进行动态预测,提出了主成分分析法（PCA）结合改进的果蝇算法（MFOA）优化GRNN的绝对瓦斯涌出量的预测手段。运用PCA算法对原始输入数据降维;并且对果蝇算法中的Si函数增加一个跳脱参数B,避免局部最优因子对预测模型的干扰;将MFOA算法对GRNN的平滑因子σ进行优化;将PCA结果作为模型的输入,建立了PCA-MFOA-GRNN算法的回采工作面瓦斯涌出量动态预测模型,结合实际矿井瓦斯涌出量监测的相关数据检验该模型,并将该模型的预测结果与未修正的FOA-GRNN算法、CIPSO-ENN算法、BP神经网络预测、Elman网络预测结果进行对比,结果表明：该预测模型对GRNN的参数优化后得到的预测模型较其他预测模型有更强的泛化能力和更高的预测精度。     

基于LLE与BA-Elman的瓦斯涌出量动态预测研究

针对瓦斯涌出量受诸多因素影响,彼此间存在复杂的非线性关系导致预测精度不高这一问题,提出基于相关分析理论和局部线性嵌入理论的Elman网络瓦斯涌出量动态预测方法。在对监测指标进行相关性分析的基础上,用局部线性嵌入理论实现瓦斯涌出量影响因素从高维空间至低维空间的映射,进而重构影响瓦斯涌出量的有效因子,并将其作为Elman网络预测模型的输入矢量,以降低模型结构的复杂度,同时用蝙蝠算法全局优化Elman模型以提高预测的精度和泛化能力。试验结果表明该动态预测模型泛化能力强,预测精度高,适用于实际工作中对瓦斯涌出量的预测。     

基于径向基的瓦斯涌出量灰色预测模型

为了进一步预防煤层瓦斯突出,实现准确、快速预测煤矿瓦斯涌出量的大小,首先采用1-AGO对样本数据进行处理,建立灰色(GM)预测模型,再利用径向基(RBF)神经网络对灰色预测模型结果进行预测,以作为其最终的预测值;采用阜新煤矿某工作面瓦斯涌出量的历史数据进行建模,实验结果表明,GM-RBF组合模型在预测精度及训练误差方面均优于单一的GM模型和RBF神经网络预测模型;算法计算简便,减弱了数据的随机性及模型误差,煤矿瓦斯涌出量的预测平均误差减小到1.57%。     

基于MPSO-RBF的瓦斯涌出量预测研究

我国煤矿的重大灾害事故中70%以上是瓦斯事故,煤矿瓦斯是影响煤矿安全生产的重要因素;针对瓦斯煤尘爆炸和煤与瓦斯突出给煤炭矿山带来的危害极大的问题,引入了基于改进PSO算法的RBF神经网络的混合优化算法(MPSO-RBF算法),即将PSO算法的全局搜索能力和RBF神经网络局部优化相结合,并建立了瓦斯预测模型;仿真与实际数据验证表明,优化算法所求的最优解具有良好的收敛能力,瓦斯涌出量的预测结果与实际值的误差在+1.44%至-0.63%之间,改进的粒子群算法优化的RBF神经网络对瓦斯涌出量预测能达到良好的效果。     

基于bi-LWCA-ENN煤与瓦斯突出危险性预测

为了提高煤与瓦斯突出的预测精度,以实现准确、可靠的瓦斯突出危险性预测,提出一种双层狼群算法（LWCA）优化Elman神经网络模型进行模式分类与预测,建立煤与瓦斯突出的双层LWCA-ENN预测模型。分析煤与瓦斯突出机理和影响因素,提取相关数据样本,筛选稳定的特征子集作为特征向量训练模型,算法通过对Elman神经网络的权值、阈值寻优,建立了基于bi-LWCA-ENN算法的预测模型并结合矿井监测数据进行实例分析。试验结果表明：煤与瓦斯突出的bi-LWCA-ENN模型稳定性好,收敛速度快,有效地实现了瓦斯突出危险性预测。     

基于CSO-RVM的瓦斯涌出量预测模型研究

为了实时监测和精准预测煤矿回采工作面绝对瓦斯涌出量,提出猫群算法(CSO)优化相关支持向量机(RVM)的绝对瓦斯涌出量预测方法.相关向量机的组合核函数可实现多特征空间的信息融合,为有限样本、高维数瓦斯涌出量预测建模问题提供一种行之有效的方法.并用CSO算法对RVM瓦斯涌出量预测模型的核函数权重p和高斯核参数σ快速寻优.利用矿井无线传感器网络检测到的各项历史数据试验.结果表明,相比BP、SVM算法,该耦合模型有效提高了预测精度,具有更好的泛化能力,为矿井瓦斯预测提供理论支持.     

煤矿井下安全监控分站的设计及其仿真实现

为了设计一种具有低成本、低功耗、易操作、功能强且可靠性高的煤矿井下安全分站,针对煤矿安全生产实际,文章提出了采用MCS-51系列单片机为核心、具有CAN总线通信接口的煤矿井下安全监控分站的设计方案;首先给出煤矿井下安全监控分站的整体构架设计,然后着重阐述模拟量输入信号处理系统的设计过程,最后说明单片机最小系统及其键盘、显示、报警、通信等各个组成部分的设计;为验证设计方案的可行性与有效性,使用Proteus软件对设计内容进行仿真验证,设计的煤矿井下安全监控分站具有瓦斯、温度等模拟量参数超标报警功能和电机开停、风门开闭等开关量指示功能;仿真结果表明:设计的煤矿井下安全监控分站具有一定的实际应用价值.     

View-based model-driven architecture for enhancing maintainability of data access services

In modern service-oriented architectures, database access is done by a special type of services, the so-called data access services (DAS). Though, particularly in data-intensive applications, using and developing DAS are very common today, the link between the DAS and their implementation, e.g. a layer of data access objects (DAOs) encapsulating the database queries, still is not sufficiently elaborated, yet. As a result, as the number of DAS grows, finding the desired DAS for reuse and/or associated documentation can become an impossible task. In this paper we focus on bridging this gap between the DAS and their implementation by presenting a view-based, model-driven data access architecture (VMDA) managing models of the DAS, DAOs and database queries in a queryable manner. Our models support tailored views of different stakeholders and are scalable with all types of DAS implementations. In this paper we show that our view-based and model driven architecture approach can enhance software development productivity and maintainability by improving DAS documentation. Moreover, our VMDA opens a wide range of applications such as evaluating DAS usage for DAS performance optimization. Furthermore, we provide tool support and illustrate the applicability of our VMDA in a large-scale case study. Finally, we quantitatively prove that our approach performs with acceptable response times.     

A pragmatic multi-method investigation of discrepant technological events: Coping,attributions, and ‘accidental’ learning

Discrepant technological events or situations that entail a problem, a misunderstanding or a difficulty with the Information Technology (IT) being employed, are common in the workplace, and can lead to frustration and avoidance behaviors. Little is known, however, about how individuals cope with these events. This paper examines these events by using a multi-method pragmatic approach informed by coping theory. The results of two studies – a critical incident study and an experiment – serve to build and test, respectively, a theoretical model that posits that individuals use a variety of strategies when dealing with these events: they experience negative emotions, make external attributions, and adopt engagement coping strategies directed at solving the event, eventually switching to a disengagement coping strategy when they feel they have no control over the situation. Furthermore, users’ efforts may result in ‘accidental’ learning as they try to overcome the discrepant IT events through engagement coping. The paper ends with a discussion of the results in light of existing literature, future opportunities for research, and implications for practice.     

The Distributed Time-Triggered Simulation Scheme: Core Principles and Supporting Execution Engine

Distributed real-time simulation is a young technology field but its practice is under increasing demands. In recent years the author and his collaborators have been establishing a new approach called the distributed time-triggered simulation (DTS) scheme which is conceptually simple and easy to use but widely applicable. The concept was initiated in the course of developing a new-generation object-oriented real-time programming scheme called the time-triggered message-triggered object (TMO) programming scheme. Some fundamental issues inherent in distributed real-time simulation that were learned during recent experimental studies are discussed along with some approaches for resolving the issues. An execution engine developed to support both the TMOs engaged in control computation and the TMOs engaged in DTS is also discussed along with its possible extensions that will enable significantly larger-scale DTSs.     

Software-Konfigurationsmanagement in großen Softwareprojekten

Zusammenfassung Mit zunehmender Größe der Softwaresysteme verschärfen sich die für die Software-Herstellung typischen Probleme: Beherrschen großer Objektmengen, Erhalten der Systemkonsistenz, Kontrolle der ständigen Änderungseinflüsse und Gewährleisten einer langen Lebensdauer. Die Disziplin Konfigurationsmanagement bildet den methodischen Ansatz, diese Probleme besser zu beherrschen. Software-Konfigurationsmanagement faßt die Herstellung von Softwaresystemen als eine Abfolge von kontrollierten Änderungen an gesicherten Zwischen- und Endergebnissen auf. Dargestellt werden die Objekte und Funktionen des Software-Konfigurationsmanagements sowie die hierfür in großen Software-Projekten benötigten Methoden, Instanzen und Hilfsmittel.     

Information for Authors

正http://www.zju.edu.cn/jzus http://www.springerlink.com Aim The Journals of Zhejiang University-SCIENCE(A/B/C)are edited by the international board of distinguished Chinese and foreign scientists,and are aimed to present the latest developments and achievements in scientific research in China and     

Editorial:Brain-machine interface(BMI) and cyborg intelligence

正Brain-machine interfaces(BMIs)aim at building a direct communication pathway between the brain and an external device,and represent an area of research where significant progress has been made during the past decade.Based on BMIs,mind information can be read out by neural signals to control     

Information for Authors

正http://www.zju.edu.cn/jzus http://www.springerlink.com Aim The Journals of Zhejiang University-SCIENCE(A/B/C)are edited by the international board of distinguished Chinese and foreign scientists,and are aimed to present the latest developments and achievements in scientific research in China and overseas to the world’s scientific circles,especially to stimulate     

SCIENCE CHINA Information Sciences SUBJECT INDEX TO VOLUME 57(2014)

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Information for Authors

正http://www.zju.edu.cn/jzus http://www.springerlink.com Aim The Journals of Zhejiang University-SCIENCE(A/B/C)are edited by the international board of distinguished Chinese and foreign scientists,and are aimed to present the latest developments and achievements in scientific research in China and overseas to the world’s scientific circles,especially to stimulate