Early Warning Analysis Research Articles

Early Warning Analysis of Oil-Gas Field Equipment Failure Based on Statistical Model of Failure Rate

This paper proposed a method used to estimate mechanical equipment failure for oil-gas field enterprises. First, this method established a mixed failure distribution model of mechanical equipment modules. According to the collected statistical data of module defect, this method then applied MLE (Maximum Likelihood Estimation) algorithm and EM algorithm to calculate the mixed failure distribution parameters of mechanical equipment modules, so as to form the mixed distribution function of failure rate. The feasibility of this method was verified through examples. At last, the concept of risk early warning management for mechanical equipment failure was proposed based on the estimated distribution function of mechanical equipment failure rate. As a result, there are scientific grounds for the equipment maintenance & repair cycle regulated by grass-roots staff and the equipment replacement & scrapping strategy set by management-level users.

EU Crisis Management and Conflict Prevention. Identifying Institutional Constrains for Early Warning Analysis Utilization

Early warning systems are responsible for monitoring crisis situations and generating warning signals of situations which escalate and pose a threat to international and human security. The application of this instrument often determines the success of preventive measures and efficiency of further crisis management. Being aware of the importance of this mechanism, the EU invested substantial resources for its development within the CFSP/CSDP framework. Nonetheless, the faulty institutional design and insufficient analytical capabilities of the EEAS put the applicability of the system in question. The article analyses a set of early warning institutional arrangements embedded in CFSP/CSDP institutional structure. In doing so, it describes and examines the main institutional and systemic constrains of early warning utilization in the EU conflict prevention framework.

Submerged area of typical torrential flood and debris-flow disasters in Mengzong Gully, China

ABSTRACTThe torrential flooding and debris flow disasters associated with global climate change pose not only serious threat to individual lives and property, but also impact economic development. Accurately simulating flood scenarios can help to reduce the losses caused by torrential flooding and debris flow by making early warning, evacuation planning, and risk analysis possible. In this study, HEC-RAS software and HEC-GeoRAS module were employed in GIS (geographic information system) to simulate the flood overtopping in the Mengzong Gully of Batang River in flood scenarios occurring once in 20, 50, and 100 years, respectively. The simulated floods provided valuable information including scope and depth of submersion via 2D visualization.

A Diagnostic System for State Grid’s Operations

This paper introduces the key technologies in the company’s operations and diagnostic analysis methods, including data adjustment method, empirical modeling, structural analysis, trend analysis and forecasting techniques, early warning analysis techniques. Then, empirical models was built based on the operational data of State Grid, the performance of the company was assessed to help find the problems in operations. This study provided the basis for the enterprises to monitor and understand the influence of the market or other external factors on the implementation of the enterprise strategic objectives.

Application and Study on Tailing Dam Saturation-Line Detection of Based on Ground Penetrating Radar Technology

Just because that saturation-line is a primary factor to affect both security and stability of tailing dam, it is of great significance to define its location and distribution. Different sand body moisture contents of the saturation-line up-down layer lead to distinctive dielectric properties, which is essential to geophysical prospecting. On the basis of feasibility study of using radar to detect the saturation-line, the approach is applied practically in one dam. It is predicted that Ground Penetrating Radar (GPR) not only has a series of advantages such as intuitional image, shorter work period, greater detective depth, less interference to dams; but also can acquire calculable depth and location data, which can forecast holistic distribution of the saturation-line from a macroscopic view, and of which diagrams and results can offer reliable basis on safety, early warning analysis and security management.

Study of post marketing safety reevaluation of Shenqi Fuzheng injection

In order to promote the Shenqifuzheng injection (SQFZ) clinical medication safety, this study reevaluate on SQFZ post marketing safety study systematically. Including multi center large sample registration type safety monitoring research, the analysis based on national spontaneous reporting system data, the analysis based on the 20 national hospital information system data and literature research. Above the analysis, it suggests that SQFZ has good security. The more adverse drug reaction (ADR) as allergic reactions, mainly involved in the damage of skin, appendages and its systemic damage, serious person can appear allergic shock. ADR/E is more common in the elderly, may be related to medication (tumor) populations. Early warning analysis based on SRS data and literature research are of the view that "phlebitis" has a strong association with SQFZ used.

5T Information Fusion System Based on Train Technology Scheme Design

On the train parts of 5T system at present all kinds of fault real-time monitoring the relevance of the weak, poor information sharing, centralized monitoring problem not in time, the system can achieve 5T effective integration of information system, information real-time delivery, 5T information fusion and 5T information fusion integrated query and statistical analysis and other functions through the acquisition of 5T system data, to train vehicle as the object of management, in order to train car number as the only connection identifier, fusion 5T information resources, to establish a global view of train vehicle safety monitoring information model. To improve the level of security early warning and fault analysis ability is of great significance.

Design and Realization on the Financial Early-Warning Analysis System of Colleges and Universities Based on Data Mining Technology

In this thesis, in order to solve the student arrearage problems in colleges and universities, risk weight factor is introduced to improve ID3 algorithm through the research on data mining technology and the combination with financial management system of colleges and universities so that ID3 decision-making tree algorithm can classify based on the risk weights of all the factors of the financial data; the early warning system scheme on the student arrearage problems in colleges and universities is designed so as to predict the high-risk defaulting students dynamically and accurately and lay scientific foundations for avoiding financial risk in colleges and universities.

Early warning signs in social-ecological networks.

A number of social-ecological systems exhibit complex behaviour associated with nonlinearities, bifurcations, and interaction with stochastic drivers. These systems are often prone to abrupt and unexpected instabilities and state shifts that emerge as a discontinuous response to gradual changes in environmental drivers. Predicting such behaviours is crucial to the prevention of or preparation for unwanted regime shifts. Recent research in ecology has investigated early warning signs that anticipate the divergence of univariate ecosystem dynamics from a stable attractor. To date, leading indicators of instability in systems with multiple interacting components have remained poorly investigated. This is a major limitation in the understanding of the dynamics of complex social-ecological networks. Here, we develop a theoretical framework to demonstrate that rising variance—measured, for example, by the maximum element of the covariance matrix of the network—is an effective leading indicator of network instability. We show that its reliability and robustness depend more on the sign of the interactions within the network than the network structure or noise intensity. Mutualistic, scale free and small world networks are less stable than their antagonistic or random counterparts but their instability is more reliably predicted by this leading indicator. These results provide new advances in multidimensional early warning analysis and offer a framework to evaluate the resilience of social-ecological networks.

Assessment of earthquake early warning potential from KiK-Net seismograms of 2000–2011 north and east Japan events

An earthquake early warning alert system with a high degree of accuracy and precision and measured in sec- ondsisanecessitytosavemanylivesinJapan,oneofthemost tectonically active countries of the world. In this paper, we estimate the potential value for early warning of crustal and intraslab earthquakes recorded at the KiK-Net network. The work reported here is similar to that of the early warning analysis system of California and uses new comprehensive data from northernand eastern Japan (Hokkaido and Tohoko). Anattempt wasalsomadetoanalyzewhether theslopesofthe regression lines obtained for Californian events (all are crustal events)aresimilar tothoseobtainedfor HokkaidoandTohoko crustal events. We also analyzed whether there was any dif- ferencebetweentheslopesobtainedfromJapanesecrustaland intraslab earthquakes. The poor correlation of the higher mag- nitude (>5.5) with predominant period implied quite a limited predictability of predominant frequency. This was evident from the scattered plot obtained for higher magnitudes. These limitations led us to use a discrete wavelet transform- automated algorithm based on the bi-orthogonal construction with two and four vanishing moments for the primal and dual wavelets.

The Early Warning and Algorithm Implementation of Urban Road Submergence in Heavy Rains

The heavy rains often have big impact on urban traffic, it causes a lot of water cumulating at road depression, which affects normal thoroughfare for vehicles and even brings about road congestion. In this paper, seed spread algorithm to determine the catchment area is improved through analyzing the topographic feature of the underpass bridge and its surrounding small area of city typical low-lying areas, and it solves the inaccurate problem of catchment area of small area. According to forecast information of meteorological department, accumulated water region is quickly simulated and the water depth is dynamically displayed through the analysis of the process of road depression flooded by heavy rains and the calculation of water-collecting amount and depth, which can provide early warning information. Through the secondary development of 3D GIS software-skyline, the early flood warning analysis and dynamic simulation of the urban rainstorm is implemented, which can provide decision support for traffic warning and emergency response.

Research on the Application of Computer Data Mining Technology in the Financial Risk Management

The computer data mining technology plays an important role in the financial risk management. It can extract the implicit data that people don't know in advance, in the mean time, and potentially useful information and knowledge for managers to provide decision-making reference. This paper introduces the concept of data mining, the process and main technology first, and then introduces the typical application of data mining in the financial risk management, such as customer relationship management, credit risk assessment and financial crisis early warning analysis. At last, it has a summary to provide the risk management for the financial industry.

Observational Studies and a Statistical Early Warning of Surface Ozone Pollution in Tangshan, the Largest Heavy Industry City of North China

Continuous measurements of surface ozone (O3) and nitrogen oxides (NOX) at an urban site (39°37′N, 118°09′E) in Tangshan, the largest heavy industry city of North China during summertime from 2008 to 2011 are presented. The pollution of O3 was serious in the city. The daily maximum 1 h means (O3_1-hr max) reached 157 ± 55, 161 ± 54, 120 ± 50, and 178 ± 75 μg/m3 corresponding to an excess over the standard rates of 21%, 27%, 10%, and 40% in 2008–2011, respectively. The total oxidant level (OX = O3 + NO2) was high, with seasonal average concentrations up to 100 μg/m3 in summer. The level of OX at a given location was made up of NOX-independent and NOX-dependent contributions. The independent part can be considered as a regional contribution and was about 100 μg/m3 in Tangshan. Statistical early warning analysis revealed that the O3 levels would exceed the standard rate by 50% on the day following a day when the daily average ozone concentration (O3_mean) exceeded 87 μg/m3 and the daily maximum temperature (T_max) exceeded 29 °C. The exceed-standard rate would reach 80% when O3_mean and T_max exceeded 113 μg/m3 and 31 °C. Similarly, the exceed-standard rate would reach 100% when O3_mean and T_max exceeded 127 μg/m3 and 33 °C, respectively.

Early warning analysis for social diffusion events

There is considerable interest in developing predictive capabilities for social diffusion processes, for instance to permit early identification of emerging contentious situations, rapid detection of disease outbreaks, or accurate forecasting of the ultimate reach of potentially “viral” ideas or behaviors. This paper proposes a new approach to this predictive analytics problem, in which analysis of meso-scale network dynamics is leveraged to generate useful predictions for complex social phenomena. We begin by deriving a stochastic hybrid dynamical systems (S-HDS) model for diffusion processes taking place over social networks with realistic topologies; this modeling approach is inspired by recent work in biology demonstrating that S-HDS offer a useful mathematical formalism with which to represent complex, multi-scale biological network dynamics. We then perform formal stochastic reachability analysis with this S-HDS model and conclude that the outcomes of social diffusion processes may depend crucially upon the way the early dynamics of the process interacts with the underlying network’s community structure and core-periphery structure. This theoretical finding provides the foundations for developing a machine learning algorithm that enables accurate early warning analysis for social diffusion events. The utility of the warning algorithm, and the power of network-based predictive metrics, are demonstrated through an empirical investigation of the propagation of political “memes” over social media networks. Additionally, we illustrate the potential of the approach for security informatics applications through case studies involving early warning analysis of large-scale protests events and politically-motivated cyber attacks.

Establishment of Accident Risk Early-Warning Macroscopic Model on Ventilation, Gas, Dust and Fire in Coal Mine

Based on the accident prevention of ventilation, dust, gas and fire in coal mines, and the risk early-warning theory of accidents, the risk early-warning principle is promoted. Then according to the principle and organization, combining the minds of safety scientific theory, cybernetics, information theory, decision theory and systems theory, using the computer technology, the macroscopic model of risk early-warning on ventilation, gas, dust and fire in coal mines is established. Then, it analyzed and introduced the process of detection, identification, early-warning analysis, early-warning signal distribution, and early-warning measures in the model. Finally, it developed a risk early-warning management information system on coal mine accidents by the model.

An Empirical Study on Predicting Financial Risk of Listed Companies

Financial crisis early warning analysis is a matter of grave social and economic concern. It is important for enterprises, commercial banks and various investors. This is an exploratory study to determine if financial ratios of crisis companies differ from those of no crisis companies. The crisis firms (n=63) were then matched with no crisis firms on the basis of firm size, time period, and industry. Using this matched-pairs design, choose 63 listed companies, which are marked ST companies because of abnormal financial standing in Shanghai and Shenzhen in 2006, form the financial crisis samples, and choose some similar sized listed companies in same industry as matching samples, Taking the index of property liabilities ratio, audit opinion, finance lever ratio, gross property net profit ratio, sales revenue growth ratio and cash flux to current liability ratio as the final variants, set up the discriminant model by Fisher’ coefficient, conduct the case analysis of financial crisis early warning. These results provide empirical evidence of the limited ability of financial ratios to detect and predict crisis financial reporting.

Early Warning Analysis for Humanitarian Preparedness and Conflict Prevention1

Ideally, early warning analysis provides information in a form that is easily digested by senior decision makers, serving as a first step in planning conflict prevention and preparedness measures. This paper outlines how practical early warning analysis can be undertaken, identifying seven central early warning factors within which individual indicators can be aggregated. While we can continue to search for the holy grail of early warning – that is the parsimonious polynomic equation which yields robust predictions, this should not come at the cost of the policy relevant tin cup that can be used today.