A risk assessment method considering risk attributes and work safety informational needs and its application

The technological revolution has spawned a new generation of industrial systems,but it has also put forward higher requirements for safety management accuracy,timeliness,and systematicness.Risk assessment needs to evolve to address the existing and future challenges by considering the new demands and advancements in safety management.The study aims to propose a systematic and comprehensive risk assessment method to meet the needs of process system safety management.The methodology first incorporates possibility,severity,and dynamicity(PSD)to structure the“51X”evaluation indicator system,including the inherent,management,and disturbance risk factors.Subsequently,the four-tier(risk point-unit-enterprise-region)risk assessment(RA)mathematical model has been established to consider supervision needs.And in conclusion,the application of the PSD-RA method in ammonia refrigeration workshop cases and safety risk monitoring systems is presented to illustrate the feasibility and effectiveness of the proposed PSD-RA method in safety management.The findings show that the PSD-RA method can be well integrated with the needs of safety work informatization,which is also helpful for implementing the enterprise's safety work responsibility and the government's safety supervision responsibility.

Modelling of Active and Latent Attributes Based on Traveler Perspectives: Case of Port City of Douala

A growing stream of study stresses the relevance of subjective elements in understanding the hierarchy of preferences that underpin individual travel behavior. The purpose of this study is to evaluate the impact of various factors on mode choice. To achieve this, a multinomial logit model (MNL) was used to analyze the relationships between mode choice and three classes of attributes;Combined Active and Latent, Active only and Latent only attributes. The data used are derived from surveys in the port city of Douala, Cameroon as a case study. Results stipulated that, the combined attributes model performed better than both active only attributes and latent only attributes models. Likewise, latent only attributes model performed better than active only attributes model. The advantage of modelling all three groups is for better selection of the most relevant attributes, and this is very relevant in understanding travel behavior of individuals and mode choice decisions.

Forward modeling of fracture prediction based on seismic attribute modeling

Fractured reservoirs have always been a big favorable area for oil and gas reservoirs,so prediction of fractures is also a research hotspot in recent years.Due to the diversity of fracture development and the unclear development mechanism,fracture prediction has always been a major problem.Simple numerical simulation In this paper,seismic attribute is combined with numerical simulation,logging data and actual seismic profile are used as constraints,inversion impedance value and coherent attribute are combined,and finally a property model more in line with the actual geological conditions is established.The wave equation calculation and migration processing were used to obtain the numerical simulation profile,and the actual seismic profile,fracture detection profile and numerical simulation profile were combined for analysis:①The numerical simulation section under this modeling method can greatly correspond to the actual seismic section,and the reflected results can better reflect the changes of response characteristics.②The reliability and applicability of the fracture detection technology can be determined by comparing the forward simulation profile with the fracture detection profile.

An attribute recognition model based on entropy weight for evaluating the quality of groundwater sources

In our study, entropy weight coefficients, based on Shannon entropy, were determined for an attribute recognition model to model the quality of groundwater sources. The model follows the theory previously proposed by Chen Q S. In the model, firstly, the author establishes the attribute space matrix and determines the weight based on Shannon entropy theory; secondly, calculates attribute measure; thirdly, evaluates that with confidence criterion and score criterion; finally, an application example is given. The results show that the water quality of the groundwater sources for the city comes up to the grade II or III standard. There is no pollution that obviously exceeds the standard and the water can meet people’s needs .The results from an evaluation of this model are in basic agreement with the observed situation and with a set pair analysis (SPA) model.

Risk assessment model of tunnel water inrush based on improved attribute mathematical theory

Tunnel water inrush is one of the common geological disasters in the underground engineering construction.In order to effectively evaluate and control the occurrence of water inrush,the risk assessment model of tunnel water inrush was proposed based on improved attribute mathematical theory.The trigonometric functions were adopted to optimize the attribute mathematical theory,avoiding the influence of mutation points and linear variation zones in traditional linear measurement functions on the accuracy of the model.Based on comprehensive analysis of various factors,five parameters were selected as the evaluation indicators for the model,including tunnel head pressure,permeability coefficient of surrounding rock,crushing degree of surrounding rock,relative angle of joint plane and tunnel section size,under the principle of dimension rationality,independence,directness and quantification.The indicator classifications were determined.The links among measured data were analyzed in detail,and the objective weight of each indicator was determined by using similar weight method.Thereby the tunnel water inrush risk assessment model is established and applied in four target segments of two different tunnels in engineering.The evaluation results and the actual excavation data agree well,which indicates that the model is of high credibility and feasibility.

Comprehensive Assessment of Seawater Quality Based on an Improved Attribute Recognition Model

The attribute recognition model (ARM) has been widely used to make comprehensive assessment in many engineering fields, such as environment, ecology, and economy. However, large numbers of experiments indicate that the value of weight vector has no relativity to its initial value but depends on the data of Quality Standard and actual samples. In the present study, the ARM is enhanced with the technique of data driving, which means some more groups of data from the Quality Standard are selected with the uniform random method to make the calculation of weight values more rational and more scientific. This improved attribute recognition model (IARM) is applied to a real case of assessment on seawater quality. The given example shows that the IARM has the merits of being simple in principle, easy to operate, and capable of producing objective results, and is therefore of use in evaluation problems in marine environment science.

A New Growth Model of Fault Attributes in a Strike-Slip Fault System in the Tarim Basin

Fault attributes generally display a consistent power–law-scaling relationship.Based on new 3 D seismic data,however,we found some exceptional fault attribute relationships of lengths(L)–throw(T)(vertical component of displacement),overlap zone length(Lo)–width(Wo)from a strike-slip fault system of the Ordovician carbonates in the Tarim Basin.The L–T relationship shows two linear segments with breakup at^40 km in fault length.This presents an exceptional throw increase in the second stage,which is attributed to a localization of vertical displacement and deformation in overlapping zones other than the different fault scales in a mature fault zone.The Lo–Wo relationship in the overlapping zones shows multiply stepped-shape patterns,suggesting multiple fault differential growth and periodic increase in fault size.Therefore,we propose a new alternative growth model of fault attributes in strike-slip fault zones,in which the overlapping zones accumulated localized displacement and deformation in the intracratonic strike-slip fault zone.

DEVELOPMENT OF A GIS DATA MODEL WITH SPATIAL,TEMPORAL AND ATTRIBUTE COMPONENTS BASED ON OBJECT-ORIENTED APPROACH

This paper presents a conceptual data model, the STA-model, for handling spatial, temporal and attribute aspects of objects in GIS. The model is developed on the basis of object-oriented modeling approach. This model includes two major parts: (a) modeling the signal objects by STA-object elements, and (b) modeling relationships between STA-objects. As an example, the STA-model is applied for modeling land cover change data with spatial, temporal and attribute components.

Attribute-Based Re-Encryption Scheme in the Standard Model

In this paper, we propose a new attribute-based proxy re-encryption scheme, where a semi-trusted proxy, with some additional information, can transform a ciphertext under a set of attributes into a new ciphertext under another set of attributes on the same message, but not vice versa, furthermore, its security was proved in the standard model based on decisional bilinear Diffie-Hellman assumption. This scheme can be used to realize fine-grained selectively sharing of encrypted data, but the general proxy rencryption scheme severely can not do it, so the proposed schemecan be thought as an improvement of general traditional proxy re-encryption scheme.

Time-domain compressive dictionary of attributed scattering center model for sparse representation

Parameter estimation of the attributed scattering center(ASC) model is significant for automatic target recognition(ATR). Sparse representation based parameter estimation methods have developed rapidly. Construction of the separable dictionary is a key issue for sparse representation technology. A compressive time-domain dictionary(TD) for ASC model is presented. Two-dimensional frequency domain responses of the ASC are produced and transformed into the time domain. Then these time domain responses are cutoff and stacked into vectors. These vectored time-domain responses are amalgamated to form the TD. Compared with the traditional frequency-domain dictionary(FD), the TD is a matrix that is quite spare and can markedly reduce the data size of the dictionary. Based on the basic TD construction method, we present four extended TD construction methods, which are available for different applications. In the experiments, the performance of the TD, including the basic model and the extended models, has been firstly analyzed in comparison with the FD. Secondly, an example of parameter estimation from SAR synthetic aperture radar(SAR) measurements of a target collected in an anechoic room is exhibited. Finally, a sparse image reconstruction example is from two apart apertures. Experimental results demonstrate the effectiveness and efficiency of the proposed TD.

The Sensitivity of Model Results to Specification of Network-Based Level of Service Attributes: An Application of a Mixed Logit Model to Trave Mode Choice

The need for travel demand models is growing worldwide. Obtaining reasonably accurate level of service (LOS) attributes of different travel modes such as travel time and cost representing the performance of transportation system is not a trivial task, especially in growing cities of developing countries. This study investigates the sensitivity of results of a travel mode choice model to different specifications of network-based LOS attributes using a mixed logit model. The study also looks at the possibilities of correcting some of the inaccuracies in network-based LOS attributes. Further, the study also explores the effects of different specifications of LOS data on implied values of time and aggregation forecasting. The findings indicate that the implied values of time are very sensitive to specification of data and model implying that utmost care must be taken if the purpose of the model is to estimate values of time. Models estimated on all specifications of LOS-data perform well in prediction, likely suggesting that the extra expense on developing a more detailed and accurate network models so as to derive more precise LOS attributes is unnecessary for impact analyses of some policies.

Synthetic aperture radar imaging based on attributed scatter model using sparse recovery techniques

The sparse recovery algorithms formulate synthetic aperture radar （SAR） imaging problem in terms of sparse representation （SR） of a small number of strong scatters＇ positions among a much large number of potential scatters＇ positions, and provide an effective approach to improve the SAR image resolution. Based on the attributed scatter center model, several experiments were performed with different practical considerations to evaluate the performance of five representative SR techniques, namely, sparse Bayesian learning （SBL）, fast Bayesian matching pursuit （FBMP）, smoothed 10 norm method （SL0）, sparse reconstruction by separable approximation （SpaRSA）, fast iterative shrinkage-thresholding algorithm （FISTA）, and the parameter settings in five SR algorithms were discussed. In different situations, the performances of these algorithms were also discussed. Through the comparison of MSE and failure rate in each algorithm simulation, FBMP and SpaRSA are found suitable for dealing with problems in the SAR imaging based on attributed scattering center model. Although the SBL is time-consuming, it always get better performance when related to failure rate and high SNR.

Multiple-Attribute and Multilevel Synthetic Evaluation Model and Its Application

On the basis of analyzing the flaws of present multiple-attribute and multilevel evaluation methods, concerning the problem of characteristics of the multiple-attribute and multilevel system s appraisal and the particular emphasis on the respective attributes in the evaluation process, as well as its relevance to the environment, an optimal attribute system is proposed, and the multiplicative analytic hierarchy process (MAHP) is used to obtain subjective weight coefficients and the objective weight coefficient evaluating method is given. The deviation between the index value of each level and the values of the same index of other levels are formulated, and an optimal model is gained, thus establishing the weight coefficients of the whole optimal attribute system. Furthermore, the detailed implementation procedure of this method is introduced. Besides, favorable results have been gained by applying the model to the practical problems of economic evaluation.

New approach to multiple attribute decision making with interval numbers

In an ambiguous decision domain, the evaluation values of alternatives against attributes would be interval numbers because of the inherent, uncertain property of the problems. By using a number of linear programming models, Bryson and Mobolurin propose an approach to compute attribute weights and overall values of the alternatives in the form of interval numbers. The intervals of the overall values of alternatives are then transformed into points or crisp values for comparisons among the alternatives. However, the attribute weights are different because of the use of linear programming models in Bryson and Mobolurin＇s approach. Thus, the alternatives are not comparable because different attribute weights are employed to calculate the overall values of the alternatives. A new approach is proposed to overcome the drawbacks of Bryson and Mobolurin＇s approach. By transforming the decision matrix with intervals into the one with crisp values, a new linear programming model is proposed, to calculate the attribute weights for conducting alternative ranking.

Attributing Analysis on the Model Bias in Surface Temperature in the Climate System Model FGOALS-s2 through a Process-Based Decomposition Method

This study uses the coupled atmosphere–surface climate feedback–response analysis method（CFRAM） to analyze the surface temperature biases in the Flexible Global Ocean–Atmosphere–Land System model, spectral version 2（FGOALS-s2）in January and July. The process-based decomposition of the surface temperature biases, defined as the difference between the model and ERA-Interim during 1979–2005, enables us to attribute the model surface temperature biases to individual radiative processes including ozone, water vapor, cloud, and surface albedo; and non-radiative processes including surface sensible and latent heat fluxes, and dynamic processes at the surface and in the atmosphere. The results show that significant model surface temperature biases are almost globally present, are generally larger over land than over oceans, and are relatively larger in summer than in winter. Relative to the model biases in non-radiative processes, which tend to dominate the surface temperature biases in most parts of the world, biases in radiative processes are much smaller, except in the sub-polar Antarctic region where the cold biases from the much overestimated surface albedo are compensated for by the warm biases from nonradiative processes. The larger biases in non-radiative processes mainly lie in surface heat fluxes and in surface dynamics,which are twice as large in the Southern Hemisphere as in the Northern Hemisphere and always tend to compensate for each other. In particular, the upward/downward heat fluxes are systematically underestimated/overestimated in most parts of the world, and are mainly compensated for by surface dynamic processes including the increased heat storage in deep oceans across the globe.

Method for multiple attribute decision making based on incomplete linguistic judgment matrix

With respect to the multiple attribute decision making problems with linguistic preference relations on alternatives in the form of incomplete linguistic judgment matrix, a method is proposed to analyze the decision problem. The incomplete linguistic judgment matrix is transformed into incomplete fuzzy judgment matrix and an optimization model is developed on the basis of incomplete fuzzy judgment matrix provided by the decision maker and the decision matrix to determine attribute weights by Lagrange multiplier method. Then the overall values of all alternatives are calculated to rank them. A numerical example is given to illustrate the feasibility and practicality of the proposed method.

Risk assessment of water inrush in tunnels based on attribute interval recognition theory

Water inrush is one of the most serious geological hazards in underground engineering construction.In order to effectively prevent and control the occurrence of water inrush,a new attribute interval recognition theory and method is proposed to systematically evaluate the risk of water inrush in karst tunnels.Its innovation mainly includes that the value of evaluation index is an interval rather than a certain value;the single-index attribute evaluation model is improved non-linearly based on the idea of normal distribution;the synthetic attribute interval analysis method based on improved intuitionistic fuzzy theory is proposed.The TFN-AHP method is proposed to analyze the weight of evaluation index.By analyzing geological factors and engineering factors in tunnel zone,a multi-grade hierarchical index system for tunnel water inrush risk assessment is established.The proposed method is applied to ventilation incline of Xiakou tunnel,and its rationality and practicability is verified by comparison with field situation and evaluation results of other methods.In addition,the results evaluated by this method,which considers that water inrush is a complex non-linear system and the geological conditions have spatial variability,are more accurate and reliable.And it has good applicability in solving the problem of certain and uncertain problem.

Relationships between Soil Depth and Terrain Attributes in a Semi Arid Hilly Region in Western Iran

Soil depth generally varies in mountainous regions in rather complex ways.Conventional soil survey methods for evaluating the soil depth in mountainous and hilly regions require a lot of time,effort and consequently relatively large budget to perform.This study was conducted to explore the relationships between soil depth and topographic attributes in a hilly region in western Iran.For this,one hundred sampling points were selected using randomly stratified methodology,and considering all geomorphic surfaces including summit,shoulder,backslope,footslope and toeslope;and soil depth was actually measured.Eleven primary and secondary topographic attributes were derived from the digital elevation model(DEM) at the study area.The result of multiple linear regression indicated that slope,wetness index,catchment area and sediment transport index,which were included in the model,could explain about 76 % of total variability in soil depth at the selected site.This proposed approach may be applicable to other hilly regions in the semi-arid areas at a larger scale.

Sedimentary Microfacies and Porosity Modeling of Deep-Water Sandy Debris Flows by Combining Sedimentary Patterns with Seismic Data： An Example from Unit I of Gas Field A, South China Sea

Sandy debris flow deposits are present in Unit I during Miocene of Gas Field A in the Baiyun Depression of the South China Sea. The paucity of well data and the great variability of the sedimentary microfacies make it difficult to identify and predict the distribution patterns of the main gas reservoir, and have seriously hindered further exploration and development of the gas field. Therefore, making full use of the available seismic data is extremely important for predicting the spatial distribution of sedimentary microfacies when constructing three-dimensional reservoir models. A suitable reservoir modeling strategy or workflow controlled by sedimentary microfacies and seismic data has been developed. Five types of seismic attributes were selected to correlate with the sand percentage, and the root mean square （RMS） amplitude performed the best. The relation between the RMS amplitude and the sand percentage was used to construct a reservoir sand distribution map. Three types of main sedimentary microfacies were identified： debris channels, fan lobes, and natural levees. Using constraints from the sedimentary microfacies boundaries, a sedimentary microfacies model was constructed using the sequential indicator and assigned value simulation methods. Finally, reservoir models of physical properties for sandy debris flow deposits controlled by sedimentary microfacies and seismic inversion data were established. Property cutoff values were adopted because the sedimentary microfacies and the reservoir properties from well-logging interpretation are intrinsically different. Selection of appropriate reservoir property cutoffs is a key step in reservoir modeling when using simulation methods based on sedimentary microfacies control. When the abnormal data are truncated and the reservoir properties probability distribution fits a normal distribution, microfacies-controlled reservoir property models are more reliable than those obtained from the sequence Gauss simulation method. The cutoffs for effective porosity of the debris channel, fan lobe, and natural levee facies were 0.2, 0.09, and 0.12, respectively; the corresponding average effective porosities were 0.24, 0.13, and 0.15. The proposed modeling method makes full use of seismic attributes and seismic inversion data, and also makes the property data of single-well depositional microfacies more conformable to a normal distribution with geological significance. Thus, the method allows use of more reliable input data when we construct a model of a sandy debris flow.

Investment in deepwater oil and gas exploration projects:a multi-factor analysis with a real options model

Deepwater oil and gas projects embody high risks from geology and engineering aspects, which exert substantial influence on project valuation. But the uncer- tainties may be converted to additional value to the projects in the case of flexible management. Given the flexibility of project management, this paper extends the classical real options model to a multi-factor model which contains oil price, geology, and engineering uncertainties. It then gives an application example of the new model to evaluate deepwater oil and gas projects with a numerical analytical method. Compared with other methods and models, this multi-factor real options model contains more project information. It reflects the potential value deriving not only from oil price variation but also from geology and engi- neering uncertainties, which provides more accurate and reliable valuation information for decision makers.