NEW METHOD FOR SAFETY ASSESSMENT OF PARALLEL ROUTES

A new safety assessment method for parallel routes is presented. From the aspects of safety guard system of air traffic control（ATC） and considering the flight conflict as causing event of air collision accidents, this paper fosters a four-layer safety guard of controller command, short-term conflict alerts （STCAs）, pilot visual avoidance, and traffic alert collision avoidance system（TCAS）. Then, the problem of parallel routes collision risk is divided into two parts：the calculation of potential flight conflict and the analysis of failure probability of the four-layer safety guard. A calculation model for controller interference times is induced. By using cognitive reliability and error analysis method（CREAM）,the calculation problem to failure probability of controller sequencing flight conflicts is solved and a fault tree model of guard failure of STCA and TCAS is established. Finally, the Beijing-Shanghai parallel routes are taken as an example to be calculated and the collision risk of the parallel routes is obtained under the condition of radar control. Results show that the parallel routes can satisfy the safety demands.

论LED光源的可替换性

对GB 7000.1—2015新增的可替换光源、不可替换光源和非用户替换光源配合标记的要求进行了分析,对什么情况下非用户替换光源的灯具需要"闪电符号"进行了解释并告知了其正确使用位置。

A forecasting and forewarning model for methane hazard in working face of coal mine based on LS-SVM

To improve the precision and reliability in predicting methane hazard in working face of coal mine, we have proposed a forecasting and forewarning model for methane hazard based on the least square support vector (LS-SVM) multi-classifier and regression machine. For the forecasting model, the methane concentration can be considered as a nonlinear time series and the time series analysis method is adopted to predict the change in methane concentration using LS-SVM regression. For the forewarning model, which is based on the forecasting results, by the multi-classification method of LS-SVM, the methane hazard was identified to four grades: normal, attention, warning and danger. According to the forewarning results, corresponding measures are taken. The model was used to forecast and forewarn the K9 working face. The results obtained by LS-SVM regression show that the forecast- ing have a high precision and forewarning results based on a LS-SVM multi-classifier are credible. Therefore, it is an effective model building method for continuous prediction of methane concentration and hazard forewarning in working face.

Biological Early Warning and Emergency Management Support System for Water Pollution Accident

Water pollution accidents such as chemical spill and toxicants leach have become a serious issue in the world, which brings great threats to the aquatic ecosystem and drinking water safety. To detect unknown pollutions in time and take proper emergency actions, this study developed a system which integrated an online intelligent water quality monitoring device with a high efficient water quality model. The device is based on water quality probes and biological sensors which use fish motion as indicator. The numerical model is a combination of Euler and Lagrangian approaches. Finite element method is applied to simulate the flow field in an unstructured grid, and the pollutant be- haviours are traced through particle tracking. Oracle is used to manage the basic and monitoring data, and ArcSDE as well as ArclMS technologies are applied to make the system WebGIS-based so as to improve the data receiving and dispatching efficiency. The developed system has been successfully operated in Douhe Reservoir and Taihu Lake, China, where drinking water intakes are installed. It is seen from the applications that the online intelligent water qual- ity monitoring and emergency support system can be of great support to emergency management.

Real-time lane departure warning system based on principal component analysis of grayscale distribution and risk evaluation model

A technology for unintended lane departure warning was proposed. As crucial information, lane boundaries were detected based on principal component analysis of grayscale distribution in search bars of given number and then each search bar was tracked using Kalman filter between frames. The lane detection performance was evaluated and demonstrated in ways of receiver operating characteristic, dice similarity coefficient and real-time performance. For lane departure detection, a lane departure risk evaluation model based on lasting time and frequency was effectively executed on the ARM-based platform. Experimental results indicate that the algorithm generates satisfactory lane detection results under different traffic and lighting conditions, and the proposed warning mechanism sends effective warning signals, avoiding most false warning.

Research on the corporate crisis early warning factors based on the GRA

By decomposing complex issues into factors at all levels, Grey Relativity Analysis （GRA） can effectively calculate the degree the main factors affect the goal value, and overcome the problems that the traditional mathematical statistics have such as big samples, heavy computation, and inconsistency between quantitative results and qualitative analysis. So it becomes one kind of simple, convenient and distinctive systematic analysis method. GRA is applied to corporate crisis early warning analysis. The paper through the GRA the half of this quantitative approach, explores the correlation degree among the corporate crisis early warning factors, in order to find the importance sequence of instance enterprise early warning crisis factors by calculation so as to provide a theoretical foundation for the prevention and decrease of the instance enterprise crisis.

Geo-hazards risk zonation and warning system based on GIS in Anshan City of Liaoning

Debris flow and Karst collapse are the main parts of geological disasters in Anshan, which have affected on the construction and development of the social economy of Anshan. In order to enhance the studies of rules of disaster and give scientific estimation and alarm ahead of schedule warning, we established the warning system of Anshan and investigated the corresponding software, which are based on the deep discussions about the crisis estimation and methods of prediction of disasters. In this paper, the foundation of model used in estimating the alarm ahead of schedule for disaster and the methods of appraisal for the alarm ahead of schedule are discussed. This system for warning and evaluating is the combination of the models of warning and GIS fiat roof, with so many virtues, such as complete functions, convenience, applicability, which has considered the output of data, analyses of space, chooses of model, output of production, report of information for warning and the statements and help of system.

Dynamic alarm prediction for critical alarms using a probabilistic model

Alarm systems play important roles for the safe and efficient operation of modern industrial plants. Critical alarms are configured with a higher priority and are safety related among many other alarms. If critical alarms can be predicted in advance, the operator will have more time to prevent them from happening. In this paper,we present a dynamic alarm prediction algorithm, which is a probabilistic model that utilizes alarm data from distributed control system, to calculate the occurrence probability of critical alarms. It accounts for the local interdependences among the alarms using the n-gram model, which occur because of the nonlinear relationships between variables. Finally, the dynamic alarm prediction algorithm is applied to an industrial case study.

Mainstreaming Early Warning Systems in Development and Planning Processes: Multilevel Implementation of Sendai Framework in Indus and Sahel

The third UN World Congress on Disaster Risk Reduction, held in Sendai, Japan in March 2015, agreed on a new framework to guide disaster risk reduction policy and practice for the next 15 years. The Sendai Framework for Disaster Risk Reduction 2015–2030(SFDRR) leaves important implementation issues unspecified and potentially creates both problems and opportunities for complex,multilevel governance systems in coping with hazards and disastrous events. Early warning systems(EWS), if built into the mainstream of planning for development and disaster relief and recovery, could present a significant opportunity to realize many SFDRR goals. We explore the complexities of using hydrometeorological EWS to prepare for drought and flood disasters in the densely populated communities of Pakistan’s Indus River Basin in contrast to the African Sahel’s less densely settled grasslands. Multilevel governance systems are often dominated by a topdown, technocentric, centralized management bias and have great difficulty responding to the needs of peripheral and vulnerable populations. People-centered, bottom-up approaches that incorporate disaggregated communities with local knowledge into a balanced, multilevel disaster risk management and governance structure have adramatically better chance of realizing the SFDRR goals for disaster risk reduction.

From Top-Down to “Community-Centric” Approaches to Early Warning Systems: Exploring Pathways to Improve Disaster Risk Reduction Through Community Participation

Natural hazards and their related impacts can have powerful implications for humanity, particularly communities with deep reliance on natural resources. The development of effective early warning systems(EWS) can contribute to reducing natural hazard impacts on communities by improving risk reduction strategies and activities.However, current shortcomings in the conception and applications of EWS undermine risk reduction at the grassroots level. This article explores various pathways to involve local communities in EWS from top-down to more participatory approaches. Based on a literature review and three case studies that outline various levels of participation in EWS in Kenya, Hawai'i, and Sri Lanka, the article suggests a need to review the way EWS are designed and applied, promoting a shift from the traditional expert-driven approach to one that is embedded at the grassroots level and driven by the vulnerable communities. Such a community-centric approach also raises multiple challenges linked to a necessary shift of conception of EWS and highlights the need for more research on pathways for sustainable community engagement.

Information fusion diagnosis and early-warning method for monitoring the long-term service safety of high dams

Analyzing the service behavior of high dams and establishing early-warning systems for them have become increasingly important in ensuring their long-term service.Current analysis methods used to obtain safety monitoring data are suited only to single survey point data.Unreliable or even paradoxical results are inevitably obtained when processing large amounts of monitoring data,thereby causing difficulty in acquiring precise conclusions.Therefore,we have developed a new method based on multi-source information fusion for conducting a comprehensive analysis of prototype monitoring data of high dams.In addition,we propose the use of decision information entropy analysis for building a diagnosis and early-warning system for the long-term service of high dams.Data metrics reduction is achieved using information fusion at the data level.A Bayesian information fusion is then conducted at the decision level to obtain a comprehensive diagnosis.Early-warning outcomes can be released after sorting analysis results from multi-positions in the dam according to importance.A case study indicates that the new method can effectively handle large amounts of monitoring data from numerous survey points.It can likewise obtain precise real-time results and export comprehensive early-warning outcomes from multi-positions of high dams.

Hear no evil： The effect of auditory warning signals on avian innate avoidance, learned avoidance and memory

Many aposematic insect species advertise their toxicity to potential predators using olfactory and auditory signals, in addition to visual signals, to produce a multimodal warning display. The olfactory signals in these displays may have interesting effects, such as eliciting innate avoidance against novel colored prey, or improving learning and memory of defended prey. However, little is known about the effects of such ancillary signals when they are auditory rather than olfactory. The few studies that have investigated this question have provided conflicting results. The current study sought to clarify and extend understanding of the effects of prey auditory signals on avian predator responses. The domestic chick Gallus gallus domesticus was used as a model avian predator to examine how the defensive buzzing sound of a bumblebee Bombus terrestris affected the chick＇s innate avoidance behavior, and the learning and memory of prey avoidance. The results demonstrate that the buzzing sound had no effect on the predator＇s responses to unpalatable aposematically colored crumbs, suggesting that the agitated buzzing of B. terrestris may provide no additional protection from avian predators .

Urine glucose levels are disordered before blood glucose level increase was observed in Zucker diabetic fatty rats

Many patients with diabetes are not diagnosed at all or are diagnosed too late to be effectively treated, resulting in nonspecific symptoms and a long period of incubation of the disease. Pre-diabetes is an early warning signal of diabetes, and the change of urine glucose in this period has been ignored even though urine has long been related with diabetes. In this study, Zucker diabetic fatty (ZDF) rats were used to test if there were changes in urine glucose before blood glucose increases. Six 8-week-old male ZDF rats (fa/fa) and Zucker lean (ZL) rats (fa/+) were fed with Purina 5008 high-fat diet and tested for fasting blood glucose and urine glucose. After 12 weeks of feeding, the urine glucose values of the ZL rats were normal (0–10 mmol L^(-1)), but the values of the ZDF model rats increased 10 weeks before their blood glucose levels elevated. The urine glucose values of the ZDF model rats showed a state of disorder that was frequently elevated (>10 mmol L^(-1)) and occasionally normal (0–10 mmol L^(-1)). This finding may provide an easy early screening for diabetes by long-term monitoring of urine glucose levels: pre-diabetes may be revealed by frequently disordered urine glucose levels over a period.

The Study of Model About Enterprise Crisis Early-warning Based on Fuzzy Classification and

Toward the problem of judgement of enterprise crisis degree, the paper adopts fuzzy classification and fuzzy recognition method, forms enterprise character objective function in fuzzy condition, and brings forward to solve approach of optimal fuzzy classification center matrix, optimal fuzzy recognition matrix and optimal index weight under different crisis degree. By using the method in enterprise crisis early-warning example, it can distinguish enterprise crisis degree effectively.