基于粗糙集和神经网络的煤矿瓦斯预报方法研究

瓦斯灾害监测数据是煤矿瓦斯监控和预测的基础;采用现代信息处理技术和人工智能理论对每个监测平台的多传感器检测数据,主要包括井下温度、通风景、瓦斯浓度等进行挖掘、分析、处理、综合,提取出瓦斯灾害征兆信息,并用特征矩阵表示这些征兆信息;在利用最大熵方法进行特征提取的基础上,对多源数据进行多平台信息融合,提出粗糙集和神经网络融合算法,实现了煤矿瓦斯数据预报方法,增加了矿井状态监测置信度,提高了环境监测的准确率,具有实际应用意义.     

多传感器模糊信息融合在煤矿安全中的应用

我国在矿井安全监测系统中,常采用单只传感器来实现对井下工作环境安全性的监测。为了克服单只传感器无法准确判断是否有危险发生,基于模糊集系统理论,提出一种应用多传感器模糊信息融合的监测方法,将监测设备的多只传感器所获得的信息模糊化,再将其融合,从而获取设备精确的状态估计。该方法充分利用了传感器提供的多种信息,提高了系统的识别率。实验结果表明:多传感器信息融合的识别准确率高于单传感器。因而,多传感器模糊信息融合是一种有潜力的新方法。     

模糊数据融合在煤矿采掘工作面瓦斯突出预测中的应用

针对影响煤矿瓦斯突出事故因素的综合性和不确定性,提出了一种应用多传感器模糊信息融合的采掘工作面瓦斯突出预测方法。该方法以模糊系统理论为基础,采用多个传感器实时监测煤矿井下采掘工作面的风速、瓦斯浓度和温度等参数,然后对传感器采集的多元信息进行模糊化。通过融合运算和运用决策规则,得到采掘面瓦斯状态的估计与判断,对瓦斯突出进行预警。最后选取山西省某煤矿的几组传感器监测数据进行实例分析。分析结果表明,该方法降低了误报警或不报警的发生概率,提高了瓦斯突出状态预测的准确性,改善了评判系统的不确定性,使瓦斯突出预警系统的性能得到了提升,能够为煤矿的安全生产提供保障。     

矿井动力灾害实时在线监测系统设计

针对目前煤矿深部动力灾害的监测主要依赖于在线采集装置,无法实现数据的并行和快速采样的缺点,设计了一套动力灾害实时在线监测系统。该系统利用声发射、瓦斯、应力等传感器实时监测矿井动力灾害发生过程中煤岩释放的应变能及瓦斯浓度、煤岩应力参数,利用监测站实时采集、处理各种传感器信号,通过上位机数据管理软件对特征参数指标变化规律进行有效分析、结果预警等,从而实现了对动力灾害的连续动态监测。实际应用结果证明了该系统的可靠性。     

煤岩瓦斯复合动力灾害监测预警与防控技术研究进展

煤岩瓦斯复合动力灾害是深部煤炭开采的重大安全隐患,探明其致灾机理、发展监测预警及防控技术是防治关键。提出了煤岩瓦斯复合动力灾害防治“四面体”理论,即分别从灾害分类、灾害机理、灾害预警、灾害防控4个层面概述煤岩瓦斯复合动力灾害研究进展。总结了以能量释放主体、初始瓦斯压力、载荷条件为主的复合动力灾害类型划分依据;梳理了理论分析尺度和实验室尺度下的复合动力灾害机理研究进展,发现应力路径、微裂纹动态演化和煤岩赋存地质因素临界指标是致灾机理研究的关键;概述了以前期灾害前兆信息判识、中期灾害前兆信息采集、后期灾害一体化监测预警为主线的复合动力灾害监测预警技术研究进展;揭示了复合动力灾害消能减灾一体化防控技术和多尺度分源防控关键技术科学内涵。在此基础上针对两淮矿区灾害特点,提出了深部强动载条件下复合动力灾害智能判识与预警方法和分区协同防控方法。基于当前研究进展,提出了煤岩瓦斯复合动力灾害研究亟待解决的问题,助力实现深部煤炭安全、精准、高效开采。     

基于无线多传感器信息融合的火灾检测系统

传统的火灾监控系统往往采用基于单传感器的有线信息检测与传输系统,布线不方便,环境适应性和抗干扰能力较差.本文提出了一种基于ZigBee无线多传感器网络的火灾监控系统,运用包括烟雾、温度、CO气体等多传感器感知火燃烧状态,对是否发生火灾分配不同信任度函数,利用D-S证据理论融合3种传感器信息以判断火灾状态.本文详细阐述了系统工作原理、多传感器信息、D-S证据理论等关键技术,并给出了多传感器响应燃烧实验的曲线图,理论分析和实验结果表明,该无线传感器火灾监测系统能更准确地检测火情,减少误报率,提高系统的可信度.     

掘进工作面瓦斯涌出预警传感器布置位置探讨

针对矿井现有瓦斯传感器布置主要为瓦斯爆炸、瓦斯积聚等灾害监测而布置,而非为监测工作面瓦斯涌出特征而布置的情况,探讨了一种工作面瓦斯传感器的位置布置方案。试验结果表明,将传感器布置在距离工作面5m左右的距离,其监控数据较为符合工作面真实的瓦斯涌出情况,满足监测工作面瓦斯涌出特征的需要;矿井现有的掘进工作面迎头瓦斯传感器布置方式能满足监测工作面瓦斯涌出特征的需要。     

基于软判决信息融合的多参数瓦斯监测器的设计与实现

文章介绍了一种基于软判决信息融合技术的多参数瓦斯监测器的设计,分析了用于瓦斯监测的软判决算法,阐述了该监测器的单片机实现过程,给出了实验结果。在监测瓦斯时,该监测器应用Bayes决策原理对多个传感器进行观测域区间划分和信息融合,得到了对瓦斯的最优判断结果,很好地解决了报警灵敏度和误报率之间的矛盾。     

多传感器信息融合技术及其在机器人中的应用

多传感器信息融合技术是近年发展起来的一门新兴技术,在机器人领域有着广阔的应用前景。通过融合多个传感器提供的冗余、互补或更实时的信息,可以获得系统所需的更准确和更精确的信息。主要论述了多传感器信息融合的一般方法,并对其在机器人领域的应用进行了介绍。最后指出多传感器信息融合技术在微传感器、智能传感器和自适应融合等方面的发展方向。     

基于多传感器数据融合技术的瓦斯监测系统

瓦斯灾害是煤矿中最严重的灾害之一。最近几年瓦斯事故发生频繁，瓦斯爆炸夺走了许多矿井工人的生命，给国家造成了重大经济损失。对矿井瓦斯预测预报和对瓦斯进行控制，就能大大地减少瓦斯爆炸事故和矿工的伤亡。为此采用多传感器数据融合方法，对瓦斯、温度、风速等信号数据采样，提取它们特征量，对数据进行数据融合，实现对矿井瓦斯进行实时监测、预测控制，为矿井安全生产提供了有力的保障。并经过实验已取得较好的效果。     

Disaster Monitoring of Satellite Image Processing Using Progressive Image Classification

The analysis of remote sensing image areas is needed for climate detection and management, especially for monitoring flood disasters in critical environments and applications. Satellites are mostly used to detect disasters on Earth, and they have advantages in capturing Earth images. Using the control technique, Earth images can be used to obtain detailed terrain information. Since the acquisition of satellite and aerial imagery, this system has been able to detect floods, and with increasing convenience, flood detection has become more desirable in the last few years. In this paper, a Big Data Set-based Progressive Image Classification Algorithm (PICA) system is introduced to implement an image processing technique, detect disasters, and determine results with the help of the PICA, which allows disaster analysis to be extracted more effectively. The PICA is essential to overcoming strong shadows, for proper access to disaster characteristics to false positives by operators, and to false predictions that affect the impact of the disaster. The PICA creates tailoring and adjustments obtained from satellite images before training and post-disaster aerial image data patches. Two types of proposed PICA systems detect disasters faster and more accurately (95.6%).     

DSP 和粗集理论在瓦斯突出预测系统中的应用

瓦斯突出预测是一个十分复杂的课题。本系统基于理论和实践相结合的原则,通过粗集理论和DSP技术把瓦斯突出预测理论与实践结合起来。本系统设计了基于DSP的不同频率数据采集通道,CAN总线通信,DSP数据处理;并对软件设计做了详细的介绍。通过具体实践应用,本系统能够准确的预测瓦斯突出。     

管类钣金结构接口几何信息的分析与提取

在分析国内钣金行业生产需求的基础上,研究管类钣金结构接口的板厚处 理,提出了接口几何信息提取的原理、方法和步骤,从图解、解析建模和基于数值方法的系 统开发等几方面,实现了接口几何信息的精确提取和厚板构件准确的板厚处理,其思想方法 和步骤具有一定的通用性,研究结果对于提高管类钣金结构的产品质量、降低生产成本具有 应用价值。     

灾后救援供应链信息沟通网络设计

灾后救援供应链逐渐成为一个新兴研究领域。灾害情况下如何进行及时有效的信息沟通,是灾后救援供应链各节点企业能否做出快速响应的关键所在。以图论及网络中的覆盖理论为基础,讨论灾后救援供应链网络节点企业之间覆盖的概念及特性,探讨其稳定信息沟通网络的存在性问题,基于信息沟通合作和非合作两种情况,寻求最小稳定灾后救援供应链信息沟通网络。以期对灾后救援供应链各节点企业间快速有效的信息沟通做出有益的探索。     

Framework for prioritizing geospatial data processing tasks during extreme weather events

In recent years, advanced geospatial technologies have been playing an increasingly important role in supporting critical decision makings in disaster response. One rising challenge to effectively use the growing volume of geospatial data sets is to rapidly process the data and to extract useful information. Unprocessed data are intangible and non-consumable, and often create the so-called “data-rich-but-information-poor” situation. To address this issue, this study proposed a Data Envelopment Analysis (DEA) based information salience framework to prioritize the sequence of the information processing tasks. The proposed model integrates the DEA efficiency score with a linguistic group decision process. For the input variables, computational complexity and intensity are selected to measure the difficulty in information processing. For the outputs, the performance of each processing tasks is evaluated based on the experts’ judgment on how the processing tasks satisfy the needs of decision makers. These needs are characterized by four classic disaster functions. A unique element of our proposed framework is that cone constraints are added to the DEA model based on the experts’ evaluation of the importance of the four disaster functions to model the dynamic information need. The proposed model was validated with a Hurricane Sandy based case study. The results indicate that the proposed framework is capable of prioritizing geospatial data processing tasks in a systematic manner and accelerating information extraction from disaster related geospatial data sets.     

Domain-specific sentiment analysis for tweets during hurricanes (DSSA-H): A domain-adversarial neural-network-based approach

Hurricanes are one of the most frequent and destructive disasters in the United States. The events are large scale and have relatively long-term impacts. Social networking platforms such as Twitter can provide real-time information for disaster managers and affected populations during large-scale disasters (e.g., hurricanes), but extracting useful information and interpreting data accurately for disaster management is still challenging. Sentiment analysis of social media data helps detect the concerns of affected people and understand individuals' responses on the ground at unprecedented scales, but the method is known to be domain-dependent. The same words or expressions can indicate opposite sentiments in different domains. This paper proposes a domain-specific sentiment analysis approach specifically for tweets posted during hurricanes (DSSA-H). DSSA-H can retrieve hurricane-relevant tweets with a trained supervised-learning classifier, Random Forest (RF), and classify the sentiment of hurricane-relevant tweets based on a domain-adversarial neural network (DANN). We built a dataset of tweets posted during six recent hurricanes and applied the DSSA-H approach for sentiment analysis. After evaluation, we found that each classifier (i.e., RF and DANN) outperforms baseline classifiers and that DSSA-H outperforms two high-performing general sentiment classification approaches when classifying sentiments of tweets posted during hurricanes. We also applied DSSA-H in examining sentiment patterns across six recent hurricanes in the U.S. This domain-specific sentiment analysis approach can be used by the first responders and affected communities to more accurately and rapidly detect crises and emergent events, allocate resources, and assess disaster's impact during hurricanes. DSSA-H contributes to an intelligent and adaptive disaster information system for the data-rich human and the built environment system.     

燃气营业收费管理信息系统的应用与研究

能够准确计算出燃气费、及时统计报表、快速反应客户信息是燃气行业营业收费系统的焦点问题。本文结合燃气行业的营业管理模式以及先进的业务流程分析,对营业收费管理信息系统进行了深入细致的研究,并对其系统结构、功能、设计及实现技术进行了讨论。     

燃气营业收费管理信息系统的应用与研究

能够准确计算出燃气费、及时统计报表、快速反应客户信息是燃气行业营业收费系统的焦点问题。本文结合燃气行业的营业管理模式以及先进的业务流程分析，对营业收费管理信息系统进行了深入细致的研究，并对其系统结构、功能、设计及实现技术进行了讨论。     

Energy-efficient routing technique for reliable data transmission under the background of big data for disaster region

Big data analysis and cloud computing are gaining much interest in various applications including disaster management. One of the major difficulties in the process of exchanging environmental data in the disaster affected areas has been considered as one of the emerging areas of research. This research focuses on maintaining the environmental data information management of the disaster affected areas, where the intermediate node has been used to transmit the information during transmission and an optimized routing has been used to create efficient data transmission, such as temperature, pressure, humidity, and the level of pollution within the network. The intermediate node may also be hacked during data processing. In this article, the efficient big data-based clustering technique has been proposed. In this research, the information is grouped into a cluster in every comparable node and the energy consumption has been efficiently managed with the hybrid metaheuristic optimization-based effective routing technique. The system excellence has been evaluated using the energy utilization factor, packet delivery ratio, and attack-free routing effectiveness metrics to handle environmental information on disaster affected areas.