scholarly journals Modeling Dynamic Trust and Risk Evaluation Based on High-Order Moments

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Yan Gao ◽  
Zhiyong Dai ◽  
Wenfen Liu
Keyword(s):  
Risk Evaluation ◽  
Hypothesis Test ◽  
Evaluation Model ◽  
High Order ◽  
Direct Trust ◽  
Proposed Model ◽  
Dynamic Trust ◽  
The Stability ◽  
Coefficient Of Skewness ◽  
Filter Mechanism

This paper proposes a dynamic trust and risk evaluation model based on high-order moments. The credibility of an entity is measured with trust degree and risk value comprehensively. Firstly, considering the dynamic and time decay characters of trust, a time attenuation function is defined, and direct trust is further expressed. Subsequently, in order to improve the accuracy of feedback trust, a filter mechanism is constructed to eliminate the false feedback, combining coefficient of skewness with hypothesis test. More importantly, the weights of direct trust and feedback trust are derived subjectively and adaptively with the moments and frequency of direct interactions. Furthermore, risk is evaluated with direct risk and feedback risk, which are obtained by mainly using coefficient of variation and coefficient of kurtosis. Risk value can be used to measure the stability of providing services. Simulation results show that the proposed model not only has high accuracy, but also resists effectively collusive attacks and strategic malicious behaviors.

Risk Evaluation Model of Wind Energy Investment Projects Using Modified Fuzzy Group Decision-making and Monte Carlo Simulation

2020 ◽  
pp. 097674792096322
Author(s):  
Abdolmajid Erfani ◽  
Mehdi Tavakolan
Keyword(s):  
Wind Energy ◽  
Cost Estimation ◽  
Risk Evaluation ◽  
Evaluation Model ◽  
Risk Identification ◽  
Energy Investment ◽  
Investment Projects ◽  
Proposed Model ◽  
Energy Projects ◽  
Fuzzy Group Decision Making

The recent increasing trend of investments in wind energy projects to support sustainable development requires an appropriate risk evaluation model to ensure the success of these projects. Early studies focus on opinion and discussion from subject matter experts. However, the expertise level in the subject is varied, and evaluation without considering expert competency can cause biased results. On the other hand, most of the project cost estimation models do not consider uncertainty in all cash flow parameters. In response, this article proposes a model that evaluates risks in wind energy investment projects by considering the knowledge and background of experts. Then, an integrated model of risk evaluation and cost estimation is developed. The model consists of three main stages: risk identification based on a systematic literature review (SLR); risk analysis phase 1 based on a modified fuzzy group decision-making; and risk analysis phase 2 based on a Monte Carlo simulation method. The main advantages of the proposed model are: (a) providing a comprehensive risk identification in wind energy investment projects; (b) using a modified fuzzy model to improve the risk assessment process by considering the expert competency in wind energy projects; and (c) establishing an integrated model to evaluate the cash flow of the investment. A wind farm in the Middle East is selected as the case study to examine the usability and practicality of the proposed model. The results show that the most important risks are ‘change in regulation and policy’, ‘dependency on the international market for importing raw materials’ and ‘market competitiveness’. On the other hand, the financial assessment under uncertainty shows that the profitability of the investment can be varied, and it emphasises the importance of an appropriate risk management process to guarantee the success of the investment.

The risk evaluation of PPP projects: A technique based on probabilistic linguistic terms with weakened hedges

2021 ◽  
pp. 1-19
Author(s):  
Wang Lina ◽  
Xu Zeshui
Keyword(s):  
Risk Factors ◽  
Risk Management ◽  
Risk Evaluation ◽  
Risk Perceptions ◽  
Evaluation Model ◽  
Risk Propensity ◽  
Linguistic Terms ◽  
Proposed Model ◽  
Evidential Reasoning Approach ◽  
Risk Loss

Risk management is a significant part of the success of a public-private partnership (PPP) project. There are four phrases for the process of risk management: Constructing a risk management environment, identifying risk factors, evaluating risk factors, and allocating risk factors. After identifying risk factors, it is imperative to analyze and evaluate critical risk factors, which can help participants formulate strategies to allocate risk factors, and thus alleviate the possible adverse results. The objectives of analyzing and evaluating risk factors focus on two aspects: The possibilities of risk occurrence and the degrees of risk loss. On behalf of determining the critical risk factors effectively, we take the probability degree and linguistic expressions into consideration to manifest experts’ perspectives. We consider critical risk factors in terms of the probability linguistic terms with weakened hedges from the evidential reasoning approach view. The linguistic terms with weakened hedges are applied to express the degree of risk risk loss, and the possibilities of risk occurrence collect from the probabilities of linguistic terms with weakened hedges. First, the commonality function and plausibility function are applied to correct the possibilities of risk occurrence for linguistic terms with weakened hedges. Next, we build a risk evaluation model from experts’ risk propensity and risk perceptions. Moreover, a case study of the risk analyzing and evaluating process of a PPP project is applied to illustrate the availability and effectiveness of the proposed model. We contrast the introduced model with other approaches. Finally, the advantages of this model intended to improve the linguistic terms with weakened hedges for the probabilistic linguistic terms with weakened hedges and evaluate risk factors considering the evidence reasoning approach.

The Risk Evaluation Model of Open-Pit Mine Slope

2014 ◽  
Vol 501-504 ◽  
pp. 376-379
Author(s):  
Xiao Guo Chen
Keyword(s):  
Risk Evaluation ◽  
Evaluation Method ◽  
Evaluation Model ◽  
Open Pit Mine ◽  
Open Pit ◽  
Analysis Method ◽  
The North ◽  
Practical Engineering ◽  
Mining Design ◽  
The Stability

This article uses the extenics and fuzzy hierarchy analysis method,on the slope stability analysis method of uncertainty is improved.At the same time,on the measured data report AnTaiBao open-pit mine for practical engineering,using the method of the north, a risk evaluation is made on the stability of slope area.Through the engineering example of calculation shows that,in this paper, the risk evaluation method is reliable and can be used to guide the slope mining design.

scholarly journals An Efficient Dynamic Trust Evaluation Model for Wireless Sensor Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Zhengwang Ye ◽  
Tao Wen ◽  
Zhenyu Liu ◽  
Xiaoying Song ◽  
Chongguo Fu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Evaluation Model ◽  
Trust Model ◽  
Weighted Averaging ◽  
Wireless Sensor ◽  
Trust Evaluation ◽  
Direct Trust ◽  
Dynamic Trust ◽  
Indirect Trust

Trust evaluation is an effective method to detect malicious nodes and ensure security in wireless sensor networks (WSNs). In this paper, an efficient dynamic trust evaluation model (DTEM) for WSNs is proposed, which implements accurate, efficient, and dynamic trust evaluation by dynamically adjusting the weights of direct trust and indirect trust and the parameters of the update mechanism. To achieve accurate trust evaluation, the direct trust is calculated considering multitrust including communication trust, data trust, and energy trust with the punishment factor and regulating function. The indirect trust is evaluated conditionally by the trusted recommendations from a third party. Moreover, the integrated trust is measured by assigning dynamic weights for direct trust and indirect trust and combining them. Finally, we propose an update mechanism by a sliding window based on induced ordered weighted averaging operator to enhance flexibility. We can dynamically adapt the parameters and the interactive history windows number according to the actual needs of the network to realize dynamic update of direct trust value. Simulation results indicate that the proposed dynamic trust model is an efficient dynamic and attack-resistant trust evaluation model. Compared with existing approaches, the proposed dynamic trust model performs better in defending multiple malicious attacks.

scholarly journals Risky Driving Behavior Recognition Based on Vehicle Trajectory

2021 ◽  
Vol 18 (23) ◽  
pp. 12373
Author(s):  
Shengdi Chen ◽  
Qingwen Xue ◽  
Xiaochen Zhao ◽  
Yingying Xing ◽  
Jian John Lu
Keyword(s):  
Risk Evaluation ◽  
Evaluation Model ◽  
Driving Behavior ◽  
Trajectory Data ◽  
Behavior Recognition ◽  
Risky Driving ◽  
Car Following ◽  
Proposed Model ◽  
Behavior Based ◽  
Risk Evaluation Model

This paper proposes a measurement of risk (MOR) method to recognize risky driving behavior based on the trajectory data extracted from surveillance videos. Three types of risky driving behavior are studied in this paper, i.e., speed-unstable driving, serpentine driving, and risky car-following driving. The risky driving behavior recognition model contains an MOR-based risk evaluation model and an MOR threshold selection method. An MOR-based risk evaluation model is established for three types of risky driving behavior based on driving features to quantify collision risk. Then, we propose two methods, i.e., the distribution-based method and the boxplot-based method, to determine the threshold value of the MOR to recognize risky driving behavior. Finally, the trajectory data extracted from UAV videos are used to validate the proposed model. The impact of vehicle types is also taken into consideration in the model. The results show that there are significant differences between threshold values for cars and heavy trucks when performing speed-unstable driving and risky car-following driving. In addition, the difference between the proportion of recognized risky driving behavior in the testing dataset compared with that in the training dataset is limited to less than 3.5%. The recognition accuracy of risky driving behavior with the boxplot- and distribution-based methods are, respectively, 91% and 86%, indicating the validation of the proposed model. The proposed model can be widely applied to risky driving behavior recognition in video-based surveillance systems.

Supply Chain Risk Evaluation Based on Bayesian Network

2013 ◽  
Vol 680 ◽  
pp. 550-553
Author(s):  
Bo Chao Liu
Keyword(s):  
Risk Assessment ◽  
Supply Chain ◽  
Bayesian Network ◽  
Risk Evaluation ◽  
Evaluation Model ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Supply Chain Risk ◽  
Logistics Company ◽  
Proposed Model

The evaluation for supply chain risk is very important to show the latent risk and eliminate the risk. In the study, Bayesian network is proposed to evaluate the supply chain risk. The assessment indexes of supply chain risk are analyzed before supply chain risk assessment. Then, the assessment indexes of supply chain risk can be used to construct the supply chain risk assessment model. We apply a certain logistics company to study the evaluation ability of Bayesian network evaluation model proposed here. The experimental results prove the effectiveness of the proposed model.

scholarly journals Driving Behavior Based Relative Risk Evaluation Using a Nonparametric Optimization Method

2021 ◽  
Vol 18 (23) ◽  
pp. 12452
Author(s):  
Qiong Bao ◽  
Hanrun Tang ◽  
Yongjun Shen
Keyword(s):  
Relative Risk ◽  
Risk Evaluation ◽  
Absolute Risk ◽  
Evaluation Model ◽  
Optimization Method ◽  
Driving Behavior ◽  
Risky Driving ◽  
Driving Behaviors ◽  
Proposed Model ◽  
Risky Driving Behaviors

Evaluating risks when driving is a valuable method by which to make people better understand their driving behavior, and also provides the basis for improving driving performance. In many existing risk evaluation studies, however, most of the time only the occurrence frequency of risky driving events is considered in the time dimension and fixed weights allocation is adopted when constructing a risk evaluation model. In this study, we develop a driving behavior-based relative risk evaluation model using a nonparametric optimization method, in which both the frequency and the severity level of different risky driving behaviors are taken into account, and the concept of relative risk instead of absolute risk is proposed. In the case study, based on the data from a naturalistic driving experiment, various risky driving behaviors are identified, and the proposed model is applied to assess the overall risk related to the distance travelled by an individual driver during a specific driving segment, relative to other drivers on other segments, and it is further compared with an absolute risk evaluation. The results show that the proposed model is superior in avoiding the absolute risk quantification of all kinds of risky driving behaviors, and meanwhile, a prior knowledge on the contribution of different risky driving behaviors to the overall risk is not required. Such a model has a wide range of application scenarios, and is valuable for feedback research relating to safe driving, for a personalized insurance assessment based on drivers’ behavior, and for the safety evaluation of professional drivers such as ride-hailing drivers.

scholarly journals A Neutrosophic Forecasting Model for Time Series Based on First-Order State and Information Entropy of High-Order Fluctuation

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 455 ◽  
Author(s):  
Hongjun Guan ◽  
Zongli Dai ◽  
Shuang Guan ◽  
Aiwu Zhao
Keyword(s):  
Time Series ◽  
Information Entropy ◽  
Stock Exchange ◽  
Time Series Forecasting ◽  
High Order ◽  
Stock Index ◽  
Neutrosophic Set ◽  
Forecasting Model ◽  
Forecasting Models ◽  
Proposed Model

In time series forecasting, information presentation directly affects prediction efficiency. Most existing time series forecasting models follow logical rules according to the relationships between neighboring states, without considering the inconsistency of fluctuations for a related period. In this paper, we propose a new perspective to study the problem of prediction, in which inconsistency is quantified and regarded as a key characteristic of prediction rules. First, a time series is converted to a fluctuation time series by comparing each of the current data with corresponding previous data. Then, the upward trend of each of fluctuation data is mapped to the truth-membership of a neutrosophic set, while a falsity-membership is used for the downward trend. Information entropy of high-order fluctuation time series is introduced to describe the inconsistency of historical fluctuations and is mapped to the indeterminacy-membership of the neutrosophic set. Finally, an existing similarity measurement method for the neutrosophic set is introduced to find similar states during the forecasting stage. Then, a weighted arithmetic averaging (WAA) aggregation operator is introduced to obtain the forecasting result according to the corresponding similarity. Compared to existing forecasting models, the neutrosophic forecasting model based on information entropy (NFM-IE) can represent both fluctuation trend and fluctuation consistency information. In order to test its performance, we used the proposed model to forecast some realistic time series, such as the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX), the Shanghai Stock Exchange Composite Index (SHSECI), and the Hang Seng Index (HSI). The experimental results show that the proposed model can stably predict for different datasets. Simultaneously, comparing the prediction error to other approaches proves that the model has outstanding prediction accuracy and universality.

scholarly journals Research on the Evaluation Model of Urban Tourism Management Efficiency with Uncertain Linguistic Information

2014 ◽  
Vol 2014 ◽  
pp. 1-4 ◽  
Author(s):  
Song-Mao Wang ◽  
Liang-Yan Fang ◽  
Feng Deng
Keyword(s):  
Decision Making ◽  
Evaluation Model ◽  
Multiple Attribute Decision Making ◽  
Urban Tourism ◽  
Weighted Averaging ◽  
Linguistic Information ◽  
Management Efficiency ◽  
Tourism Management ◽  
Proposed Model ◽  
Multiple Attribute

We investigate the multiple attribute decision making problems for evaluating the urban tourism management efficiency with uncertain linguistic information. We utilize the uncertain linguistic weighted averaging (ULWA) operator to aggregate the uncertain linguistic information corresponding to each alternative and get the overall value of the alternatives and, then rank the alternatives and select the most desirable one(s). Finally, a numerical example for evaluating the urban tourism management efficiency with uncertain linguistic information is used to illustrate the proposed model.

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